LHW-Net: An ensemble-based machine learning framework for brain tumor classification.

LHW-Net: An ensemble-based machine learning framework for brain tumor classification - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice PLoS One . 2026 Apr 21;21(4):e0346821. doi: 10.1371/journal.pone.0346821 Search in PMC Search in PubMed View in NLM Catalog Add to search LHW-Net: An ensemble-based machine learning framework for brain tumor classification Thireesha Suryadevara Thireesha Suryadevara 1 Department of Computer Science and Engineering, SRM University AP, Amaravati, Guntur, Andhra Pradesh, India Conceptualization, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing Find articles by Thireesha Suryadevara 1, # , Naveenkumar Mahamkali Naveenkumar Mahamkali 1 Department of Computer Science and Engineering, SRM University AP, Amaravati, Guntur, Andhra Pradesh, India Conceptualization, Data curation, Formal analysis, Methodology, Supervision, Validation, Visualization Find articles by Naveenkumar Mahamkali 1, # , Mudassir Rafi Mudassir Rafi 1 Department of Computer Science and Engineering, SRM University AP, Amaravati, Guntur, Andhra Pradesh, India 2 Department of Computer Science, College of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia Funding acquisition, Project administration, Supervision, Writing – review & editing Find articles by Mudassir Rafi 1, 2, *, # Editor: Hikmat Ullah Khan 3 Author information Article notes Copyright and License information 1 Department of Computer Science and Engineering, SRM University AP, Amaravati, Guntur, Andhra Pradesh, India 2 Department of Computer Science, College of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia 3 University of Sargodha, PAKISTAN ✉ * E-mail: [email protected] , [email protected] Competing Interests: The authors have declared that no competing interests exist. # Contributed equally. Roles Thireesha Suryadevara : Conceptualization, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing Naveenkumar Mahamkali : Conceptualization, Data curation, Formal analysis, Methodology, Supervision, Validation, Visualization Mudassir Rafi : Funding acquisition, Project administration, Supervision, Writing – review & editing Hikmat Ullah Khan : Editor Received 2025 Aug 9; Accepted 2026 Mar 24; Collection date 2026. © 2026 Suryadevara et al This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PMC Copyright notice PMCID: PMC13098932  PMID: 42013111 Abstract The classification of brain tumors is an unsolved problem associated with heterogeneity of tumors and fluctuations in imaging conditions. In this work, the investigation introduces a powerful novel framework, named LHW-Net that combines handcrafted features called local binary patterns (LBP), histogram of oriented gradients (HOG), and wavelet transform (WT). Within the LHW-Net framework, the extracted features are utilized in different machine learning classifiers, such as K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Classifier (SVC). The results of the individual classifiers are further combined using probabilistic score fusion approach to improve classification performance. The effectiveness and robustness of the proposed work are validated by the achieved experimental results on commonly accepted benchmark datasets. Introduction Brain is an important organ that is at the hub of the human nervous system and collaborates with the spinal cord in order to coordinate body processes. Being a main control center, it receives the sensory information and passes the guidance with the help of complex neural networks which results in appropriate coordination of the body functions [ 1 ]. Brain disorders may significantly affect human health and brain tumors caused by abnormal tissue proliferation inside the skull are considered as one of the most dangerous and life-threatening disorders [ 2 ]. The World Health Organization (WHO) grades the type of brain tumors based on the level of severity which implies that Grade I to Grade IV brain tumors are ranked as the least to most severe respectively. Both primary and secondary brain tumors are forms of brain tumors but primary brain tumors may be either benign or malignant and they do not spread to other parts of the body but on the other hand secondary brain tumors are malignant only. Grade one tumors like the meningiomas and gliomas are largely non-threatening, whereas grade four tumors are cancerous, malicious and excessively violent [ 3 ]. Benign tumors have a slow growth rate, and neither do they spread to other parts of the body, but malignant tumors are invasive and are dangerous to the health. The brain tumors form some of the most fatal medical conditions that play significant role towards mortality in all age categories such as children, adults, and other older age groups. For the purpose of devising the most preferable plans of treatment, right classification and proper diagnosis of brain tumors are crucial and this, in turn can even enhance the survival of patients. Tumor detection and estimation is commonly done using advanced imaging tools [ 4 ] like Magnetic Resonance Imaging (MRI) [ 5 ], Single Photon Emission Computed Tomography (SPECT) [ 6 ], Positron Emission Tomography (PET) [ 7 ] and Computed Tomography (CT) [ 8 ]. Magnetic resonance imaging is an exemplary modality among the following, and its greater resolution, high contrast imaging, non-invasiveness, as well as lack of ionizing radiation, makes this extremely important as a diagnostic tool as well as monitoring the brain tumors [ 9 ]. Besides this, MRI has been an area of active research along with the development of Machine Learning and deep learning methods where the modality has a significant potential of resulting in a better diagnostic accuracy as well as the help in devising effective treatment plans to aid patients with better prognosis [ 10 ]. Despite the amount of research invested in formulating strong and precise procedures through which the automatic classification can be performed, it is still a very difficult task, because the morphology and the texture of the tumor, as well as its contrast in MRI images is, in general, very heterogeneous. As such, it prevents the development of a universal and trusty method that could be helpful in effective classification of tumors. In the endeavor to solve these problems, the current paper presents such an ensemble method where the advantages of various techniques remain united in a single form, thus covering the flaws of each other. The main contributions of the current work are as follows: The suggested framework exploits Local Binary Pattern (LBP), Histogram of Oriented Gradient (HOG) and Wavelet Transform to develop a reliable and unique characteristic descriptor. It further uses a probabilistic score combination to combine classification scores of multiple machine learning models, hence enhancing reliability. The presented fusion strategy is tested on several common standard benchmark brain tumor datasets, both binary and multiclass classification conditions were considered. The test results are compared with the modern approaches indicating that the proposed one has much better classification performance. The paper is organized as follows: In the Related Work section, the review of prior research is carried out in detail. After that, the data used and the suggested LHW-Net are described in the Materials and Methods section. Then, in the Experimental Results and Discussion section, the experimental setup, and results are presented as well as the findings discussed in terms of detail. Finally, the Conclusion section provides a conclusion to the main findings, insights and contributions that this work has made. Related work The problem of identifying and classifying brain tumors with machine learning and image processing is a complicated issue using handcrafted feature-based approaches and deep learning algorithms. This section reviews key machine learning and deep learning models for brain tumor classification (BTC). F. Ullah et al. proposed an extended evolutionary lightweight model, which is an extension of recently proposed Multimodal Lightweight XGBoost framework. It is a model particularly adapted to the recognition, and classification, of brain cancer [ 11 ]. The suggested model is tested on the dataset that is BraTS 2020 and its results are outstanding as the accuracy is 93.0%. A classification method was proposed based on medical images in order to distinguish between brain tumors and the lesions of an autoimmune disease through multi ensemble machine learning. The model utilizes texture-feature ranking, SVM learner and majority voting method to increase the accuracy of classification [ 12 ]. It is especially applicable in determining the existence of multiple sclerosis in the patients of glioma or the other way around. It is shown that the model has been successful with experimental results producing consequent training and testing accuracy of 97.96% and 97.74% respectively. One study used MRI images to classify brain tumor types using six machine learning algorithms such as RF, CN2 Rule Induction, Naive Bayes, SVM, Neural Networks and Decision Tree (DT) [ 13 ]. A collection of 253 images was used and 2048 features extracted. Support Vector Machine(SVM) showed the best results compared to other algorithms, giving an accuracy of 95.3%, improving the performance of the BTC models significantly. Normalization techniques were used in order to refine MR image quality, after that DSURF and HOG features are extracted, and they both lead to a better performance. A linear kernel SVM has been used as a classification method with the accuracy of 90.27% [ 14 ]. Kaplan et al. used an idea of detaining brain tumors applied to two strategies of feature extraction, nLBP and α LBP. The nLBP approach measures distance relationships between the neighboring pixels, whereas α LBP approach measures angular relations between the pixels. Extracted features using such techniques and by traditional LBP were tested with Linear discriminant analysis (LDA), Artificial Neural Network (ANN), K-Nearest Neighbors (KNN) and RF algorithms in a private dataset. The highest accuracy of 95.56% was attained by nLBP with d = 1 combined with the KNN classifier [ 2 ]. In [ 15 ] a new texture analysis in tumor images was proposed with integration of LBP and Gray Level Co-Occurrence Matrix (GLCM) features. SVM was then used to classify the fused features with great accuracy of 99.84% which proves the efficiency of this approach in tumor classification. Machine learning classifiers together with the deep feature extraction were used to develop a method for BTC. Brain MRI scans were processed by deep modeling to generate deep features employing transfer learning models based on pre-trained convolutional neural networks. The highest features were selected to form an ensemble and to be classified depending on the different algorithms. Three brain MRI datasets were assessed, and it was found that applying this method led to an improvement in performance by significant margins, the SVM with Radial Basis Function (RBF) kernel saw the best results, especially when dealing with large amounts of information [ 16 ]. In case of brain tumor multiclass, a deep feature fusion method was suggested where preprocessing was done using min-max normalization, but due to data scarcity, a minimal gradient ascent and severe data augmentation were adapted. The combination of features of the transfer-learned networks (namely, AlexNet, ResNet18, and GoogLeNet) was incorporated into a single enriched feature vector [ 17 ]. This has made this vector classification by SVM and KNN, thus improved classification performance. The technique outperformed the other existing systems where the accuracy was 99.7%. In [ 18 ] Wavelet Transform (DWT) is used to extract features of brain MRI images and a classification is then performed with a Convolutional Neural Network (CNN). Through experimental findings, it is found that such an approach is better than the traditional ones and it has a total accuracy of 99.3%. Materials and methods Fig 1 demonstrates the proposed methodology, LHW-Net, to be used as brain MRI image classification. LHW denotes a combination of LBP, HOG, and Wavelet features. It includes preprocessing the dataset, feature extraction, classification of the data by machine learning algorithms, and accumulation of probabilistic scores to come to final predictions. The approach was evaluated in terms of performance metrics, namely accuracy, precision, F1 score and sensitivity. The information concerning each step such as dataset features, preprocessing, feature extraction, classification, and score fusion is provided in detail in the following subsections. Fig 1. Detailed architecture of the methodology. Open in a new tab Datasets The significance of using brain MRI in recognizing and distinguishing different brain tissues makes BTC a crucial research topic for both healthcare professionals and image processing analysts. Among the many advantages that MRI scans entail, some of them are that they are not invasive, they do not use radiation hence, safe to use, the ability to take images in different directions, and they encourage multi-dimensional analysis [ 19 ]. The suggested method is using three publicly available MRI brain databases to classify a brain tumor successfully. The detailed description of the datasets along with download links is provided in S1 File . Brain tumor MRI dataset. This data is taken from three sources, Figshare [ 20 ], SARTAJ dataset [ 21 ] and Br35H dataset [ 22 ]. It includes 7,023 magnetic resonance images of human brain divided into four groups: glioma, meningioma, no tumor and pituitary tumor [ 23 ]. It should be noted that all the images identified as No Tumor were taken out of the Br35H dataset. The data has been divided into independent training and test sets as shown below: Training set: 1595 no-tumor, 1457 pituitary, 1339 meningioma, and 1321 glioma images. Testing set: 300 glioma, 405 no-tumor, 306 meningioma, and 300 pituitary images. Brain tumor image dataset. The brain tumor image dataset consists of 3064 T1-weighted contrast-enhanced MRI scans obtained by 233 patients [ 24 ]. It is subdivided into three categories of tumors consisting of meningioma which has 708 images, pituitary tumor having 930 images, and glioma with 1426 images. Br35H brain tumor detection 2020. This dataset comprises 3000 MRI images which are uniformly balanced to two categories as 1500 images are given label of Yes (tumor-positive) and 1500 of label No (tumor-negative) [ 22 ]. To make it easy to refer, hereinafter, the three datasets will be known as Dataset I, Dataset II, and Dataset III, that is similar to their respective descriptions in the above subsections. The frequency of the total number of the images per a class over these datasets is shown in Fig 2 . Fig 2. Summary of brain tumor datasets. Open in a new tab Preprocessing Data collection is followed by preprocessing which is a significant process to ensure that the images become more applicable to the intended applications. When preprocessing is done properly, it enhances accuracy, performance of the model, and generates more stable results. The essential operations performed in this research are grayscale conversion, image resizing, partitioning of datasets, and augmentation of data to balance the classes in the training set. First, images were loaded to each class separately to keep appropriate label mapping. To learn the supervised information, labels of classes were transformed into numbers. The Preprocessing steps are outlined as below: Grayscale conversion: To reduce the complexity of the data and highlight features according to their intensities, all pictures were converted to grayscale. The images were stored in the original grayscale intensity range, which is in the range of [0, 255]. Resizing: The sizes of the original images constituting each dataset were different (e.g., 512 × 512, 287 × 348, 766 × 879). All images were resized to the size of 128 × 128 pixels to guarantee consistency in the spatial representation of the images and the extraction of the features. Dataset partitioning: Where the dataset was not split, the images were split into training and testing at a ratio of 80:20. This provided adequate training data and kept a separate dataset to evaluate as shown in Fig 3 . Data augmentation: The training set was a comparatively small collection of images and unbalanced categories, so augmentation was used to introduce more variability in the data and avoid overfitting. The augmentation techniques were: Horizontal flipping: Reflecting the images by flipping horizontally. Rotation: Rotation of the images between the range of ±15° in order to mimic various orientations. Scaling: To resize the images to create some form of variation in size so that the aspect ratio remains the same. Addition of Gaussian noise: To improve invariance to variability, a random amount of noise is added to the images. Fig 3. Overview of brain tumor datasets after training and testing splitting. Open in a new tab Fig 4 shows the various image augmenting techniques. In order to prevent any chances of leakage of data, augmentation was implemented on training data only. The datasets had to be increased to be sure of adequate samples to use in training and testing, and the final values of augmented images available in each dataset are illustrated in Fig 5 . Fig 4. Visual representation of images after applying various augmentation techniques. Open in a new tab Fig 5. Overview of brain tumor datasets after augmentation. Open in a new tab Feature extraction Feature extraction is a key stage in machine learning, which converts raw data into humanly interpretable representations by isolating features describing the texture, shape, and intensity. As part of analysis of brain MRI, there is a need to obtain complementary information on the images in order to enhance classification success. In this regard, we use three highly developed descriptors which are Wavelet Transform, LBP and HOG, covering characteristically various physical attributes of the image. The Wavelet Transform in particular is an effective way of capturing multi-scale changes both in frequency and in intensity, and thus captures both global and local contrast, respectively. LBP considers intensity variations between a pixel and its neighbors to obtain the local texture patterns and thus will be very applicable in obtaining the fine textural details. The HOG technique is interested in the direction of the edges, distribution of the gradients, which encodes the shape and structure of the image. We make use of the complementarities that exist in these descriptors hence allow us to describe brain MRI images better. In-depth descriptions of every technique of feature extraction follow below. These acquired features are then fused together using the suggested fusion strategy, compared with machine learning categories and the findings are displayed and examined in Experimental Results and Discussion section. Local binary pattern. LBP is an important texture description which describes local structure of an image by contrasting the intensity of a central pixel with its neighbours. It has already been useful in texture representation, finding usage in fields like facial recognition, texture classification [ 25 ]. This study used 8 sampling points ( P = 8) and a radius of 1 ( R = 1) in extraction of LBP features with the use of the uniform encoding scheme. Flattening of resulting feature vectors into one dimension scaled arrays was done to be compatible with machine learning models. The computation of the LBP value for a pixel is expressed as shown in Equation (1) : L B P ( x c , y c ) = ∑ p = 0 P − 1 s ( g ( x p , y p ) − g ( x c , y c ) ) · 2 p (1) where: The coordinates of the center pixel are denoted by ( x c , y c ). The intensity value at this center pixel is represented by g ( x c , y c ). The intensity of the neighboring pixel indexed by p is given as g ( x p , y p ). The total number of neighboring pixels considered in a circular arrangement around the center is P . The index p refers to each neighbor and takes values from 0 to P − 1. The function s ( x ) acts as a binary threshold, defined as: s ( x ) = { 1 , if x ≥ 0 , 0 , if x < 0 . Each neighbor is assigned a weight based on its position using the factor 2 p . As described in Equation (1) , the LBP value encodes the intensity differences into a binary pattern, providing an efficient representation of the texture characteristics within the image. Histogram of oriented gradients. The HOG feature descriptor is commonly deployed in object detection, especially in discovering the intensity gradients and their directions in smaller parts of an image [ 26 ]. HOG represents objects well by encoding the orientation of gradients in terms of histograms which can be used in applications including detecting pedestrians and vehicles. The image is divided into small cells in order to derive the HOG descriptor. In every cell, the magnitudes and direction of the gradients are calculated as an estimate of each pixel. The magnitude of the gradient can be defined as Equation (2) where G ( x , y ) is the gradient magnitude. G ( x , y ) = ( ∂ g ( x , y ) ∂ x ) 2 + ( ∂ g ( x , y ) ∂ y ) 2 (2) where ∂ g ( x , y ) ∂ x and ∂ g ( x , y ) ∂ y represent the horizontal and vertical gradients of the image intensity g ( x , y ), respectively. The gradient orientation, Θ ( x , y ) , is then computed using Equation (3) : Θ ( x , y ) = tan − 1 ( ∂ g ( x , y ) ∂ y ∂ g ( x , y ) ∂ x ) (3) The pixels-per-cell parameter specifies the size of a cell in pixels (usually (8, 8)), and should be large enough to encode local gradient information. In each cell an estimation of the frequency distribution, weighted by the orientation magnitudes, is plotted as a histogram. In order to be robust to minor changes in the image transformations and changes in illumination the histograms of the different cells are combined in blocks. These blocks are defined by the size parameter cells per block which is typically (2, 2). The normalization is done in an overlapping blocks and the increased invariance of the descriptor to lighting and contrast changes. The outcome of this process is a small and distinguishing vector of features and thus HOG is an excellent technique when handling visual tasks like object detection and shape recognition. Wavelet transform. Wavelet transform can be an efficient method to decompose image into different frequencies and scales with both space and frequency information and this can be used to capture fine details and smooth patterns in images [ 27 ]. It is well used in texture analysis, edge detection and other image processing tasks. The wavelet transform breaks down an image into sub bands in terms of approximation and detail. Mathematically, DWT decomposition of an image is expressed as is displayed in Equation (4) : w ( x , y ) = ∑ s = 0 S − 1 ∑ t = 0 T − 1 ( λ s , t · ψ s , t ( x , y ) ) (4) where: The function w ( x , y ) represents the reconstructed image in the spatial domain. λ s , t are the wavelet coefficients, capturing the contribution of specific frequency components. ψ s , t ( x , y ) are the wavelet basis functions, where s controls the scale (level of detail) and t controls the position (translation). S and T denote the total number of scales and translations considered. The wavelet that we used in our analysis was the db1 which is associated with an alternative of performing a four level decomposition on a specific image, that is, the wavelet-based image decomposition. This hierarchical structure allows extracting features at various levels thus it is very appropriate in carrying out activities in which texture analysis and edge detection are utilized. Table 1 displays the training/testing set dimension with respect to LBP, Wavelet and HOG feature on three data sets, by highlighting the variances of features extraction results. Table 1. Training and testing feature dimensions across datasets. Dataset Feature Type Training Testing Dataset I LBP (6362, 16384) (1311, 16384) Wavelet (6362, 16384) (1311, 16384) HOG (6362, 8100) (1311, 8100) Dataset II LBP (3411, 16384) (613, 16384) Wavelet (3411, 16384) (613, 16384) HOG (3411, 8100) (613, 8100) Dataset III LBP (2400, 16384) (600, 16384) Wavelet (2400, 16384) (600, 16384) HOG (2400, 8100) (600, 8100) Open in a new tab Classification The classifiers that we used in this work are SVC, KNN, and Random Forest with a chosen set of parameters. The SVC [ 28 ] utilizes a linear kernel probability estimates with a set value of True ( kernel = ’linear’, probability = True ) to decide the best decision boundaries that would be used in separation of classes. KNN [ 29 ] classifier employs 3 neighbors in nearest neighborhood ( n_neighbors = 3 ) whereby each data point is classified based on majority class of their neighbors. Random Forest [ 30 ] classifier employs 100 decision trees (( n_estimators = 100 ) to enhance accuracy by combining the results of decision trees constructed on randomly chosen data and feature subsets. In order to choose these best parameters we experimented with several hyperparameter values. In the case of KNN, we used n _ neighbors = 1, 3, 5, 7, 9; with random forest, we used n _ estimators = 50, 100, 200, max _ depth = None , 10, 20, and m i n _ s a m p l e s _ s p l i t = 2 , 5 ; and with SVC, we tested kernel (linear and rbf) and regularization ( C = 0.1, 1, 10) and gamma (scale, 0.01, 0.001). Three different datasets were experimented with, and the chosen parameters, namely KNN ( n _ neighbors = 3), Random Forest ( n _ estimators = 100), and Linear SVC, were regularly performing well across all datasets which makes up the final configuration employed in the given study. Probabilistic score fusion The probabilistic score fusion model is the combination of various feature extraction algorithms, and classifiers to provide a better classification accuracy as shown in Algorithm 1. It aims to combine predictions made by various features and classifiers in order to enhance the efficiency of BTC. The approach critically examines the combinations of the classifiers residing on the various features, and determines its efficacy on the basis of the accuracy of fusion. It first examines scenarios when one classifier is used across all the features (the number of combinations is m , where m denotes the number of total classifiers as well). Algorithm 1 LHW-Net Model for Brain Tumor Classification Require: Preprocessed Images ℐ , Class Labels ℒ , Feature Extractors ℱ (LBP, HOG, Wavelets), Classifiers 𝒞 (KNN, SVC, Random Forest) Ensure: Fused Predictions 𝒫 f u s i o n Step 1: Feature Extraction 1: for each feature extractor f ∈ ℱ do 2:   Extract training features: ℱ t r a i n [ f ] ← f ( ℐ t r a i n ) 3:   Extract test features: ℱ t e s t [ f ] ← f ( ℐ t e s t ) 4: end for Step 2: Classifier Training and Prediction 5: for each classifier c ∈ 𝒞 do 6: for each feature extractor f ∈ ℱ do 7:    Train classifier: c [ f ] ← c ( ℱ t r a i n [ f ] , ℒ t r a i n ) 8:    Predict probabilities: 𝒫 [ f , c ] ← c [ f ] ( ℱ t e s t [ f ] ) 9: end for 10: end for Step 3: Probability Fusion 11: for each combination of feature-classifier pairs ( f , c ) ∈ ℱ × 𝒞 do 12:   Combine probabilities: 𝒫 c o m b i n e d ← ∏ ( f , c ) 𝒫 [ f , c ] 13: end for 14: Final predictions: 𝒫 f u s i o n ← argmax ( 𝒫 c o m b i n e d ) Step 4: Performance Evaluation 15: Calculate the performance measures, including Accuracy, Sensitivity, Precision, and F1-Score values. return 𝒫 f u s i o n The model then goes over all the combinations of classifiers in all the features to analyze all possible situations with various classifiers. Take as an example that every image in the dataset is evaluated using 27 combinations of two features and 27 combinations of three features. Such an extensive study on feature-classifier combinations guarantees a strong and desirable analysis providing overall improvements in the classification. The total number of classifier-feature combinations for two and three features is given by: Total Combinations = ( n i ) × m i (5) where n is the total number of features available, i is the number of features selected, and m is the number of classifiers. The term ( n i ) represents the number of ways to select i features from n , and m i accounts for assigning one of m classifiers to each selected feature. For example, in the case of two features ( i = 2), the total combinations are: Total Combinations for Two Features = ( n 2 ) × m 2 (6) Similarly, for three features ( i = 3), the total combinations are: Total Combinations for Three Features = ( n 3 ) × m 3 (7) The formula is able to exhaustively check all the feature-classifier combinations, as indicated in Equation (5) . In particular instances, Equations (6) and (7) outline the number of all possible combinations with 2 and 3 features respectively, promoting the robust and comprehensive study. In every combination, one uses precomputed probabilities given features based on individual feature-classifiers. These probabilities are then combined by multiplying them together across features and then the overall class prediction is chosen by getting the one with the highest overall probability. The fusion accuracy is computed in all combinations and the configuration that returns the best accuracy is determined as the best combination. This will result in effective performance of robust and accurate classification. The process is outlined as follows: Feature-Classifier Prediction: Each feature extraction technique is paired with a classifier to generate class probabilities for the test dataset. Probability Fusion: The predictions from all feature-classifier pairs are aggregated using a probabilistic fusion approach, as shown in Equation (8) . P combined = ∏ ( f , c ) P [ f , c ] (8) where P [ f , c ] represents the predicted probabilities for a specific feature-classifier pair. Final Prediction: The final class label is assigned based on the maximum value of the combined probabilities, as described in Equation (9) . P fusion = argmax ( P combined ) (9) The strength of various feature extraction techniques and classifiers are leveraged in such a way that the classification model is strong and accurate due to this fusion methodology. Through the integration of predictions of individual features, two-feature combinations and three-feature combinations, the proposed model LHW-Net eliminates the factors that limit the functioning of individual models and also their inability to improve optimal predictive performance. Performance measures The performance of the models was measured by four important parameters accuracy, precision, sensitivity and F1-score. Such metrics were computed against each feature extraction method in combination with different classifiers. Also, the LHW-Net performance was analyzed where multiple classifier combinations were applied. The in-depth review indicates the performance of individual characteristics and performance improvement achieved when the characteristics were integrated via fusion of classifiers. The underlying performance metrics are as follows: Accuracy : The correct classification of the instances with regard to total samples. Precision : This is the ratio of the correctly predicted positive to the total number predicted positive cases. Sensitivity : Also known as recall, and it is an indicator of the effectiveness of the model to identify all the positive cases. F1 score : It is an integrated measure that gives a compromise of precision and recall. Experimental results and discussion The experiments were carried out on Linux OS with x86_64 architecture, 504 GB of RAM, and an NVIDIA Tesla V100-SXM2–32GB GPU. The system was using NVIDIA driver version 555.42.02, and it supports CUDA runtime up to 12.5. Nonetheless, our experiments were performed on the Python version 3.8.5 and TensorFlow 2.4.0, which was compiled against CUDA 11.2 and cuDNN 8 and thus the computations were done under this setup. Ablation study for base learner identification The ablation study aims at determining the best possible combination of base learners and feature sets that can lead to better performance of a set of ensembles. On Dataset I, single feature–single classifier combination tests were conducted on single LBP, HOG, and Wavelets features among others to get the baseline accuracies of individual classifiers, which are SVC, KNN, and Random Forest. The paper has also examined combinations of features on a pair-wise basis (e.g., LBP + HOG, HOG + Wavelet) and three-way combinations (LBP + HOG + Wavelet) using a probabilistic method to fuse the scores to determine their combined effect. This analysis also gave insights into which features and classifier contributed the most, thus it was possible to identify combinations that could result in a much more accurate and robust ensemble of BTC. Table 2 shows the classification results attained using one feature at a time on Dataset I. Having tested the combinations, the results turned out that two-feature combinations were more accurate than the individual features. Moreover, the combination of three features gave a bit more accurate results compared to the two-features combinations, which confirms that multiple features are more beneficial to be combined. The results of the two-feature combinations and the three-feature combinations, as analyzed by probabilistic score fusion method, have been tabulated in Table 3 . Table 2. Individual feature results for Dataset I. Feature Classifier Train Accuracy (%) Test Accuracy (%) Precision (%) Sensitivity (%) F1 Score (%) LBP KNN 84.58 67.35 75.18 67.35 66.59 LBP RF 99.95 89.55 89.78 89.55 89.53 LBP SVC 99.95 90.31 90.25 90.31 90.23 HOG KNN 96.76 93.36 93.54 93.36 93.19 HOG RF 99.95 90.92 91.38 90.92 90.80 HOG SVC 99.95 93.67 93.65 93.67 93.63 Wavelet KNN 94.26 89.93 90.03 89.93 89.59 Wavelet RF 99.95 92.30 92.49 92.30 92.25 Wavelet SVC 99.95 90.31 90.25 90.31 90.28 Open in a new tab Table 3. Two-feature and three-feature combination results for Dataset I. Feature Combination Accuracy (%) Precision (%) Sensitivity (%) F1 Score (%) {LBP: KNN, HOG: KNN} 90.31 91.47 90.31 90.44 {LBP: KNN, HOG: RF} 85.89 88.65 85.89 86.18 {LBP: KNN, HOG: SVC} 87.95 90.16 87.95 88.26 {LBP: RF, HOG: KNN} 95.96 95.99 95.96 95.94 {LBP: RF, HOG: RF} 91.46 91.80 91.46 91.37 {LBP: RF, HOG: SVC} 93.52 93.60 93.52 93.47 {LBP: SVC, HOG: KNN} 96.11 96.15 96.11 96.10 {LBP: SVC, HOG: RF} 90.85 90.94 90.85 90.74 {LBP: SVC, HOG: SVC} 93.67 93.72 93.67 93.62 {LBP: KNN, Wavelet: KNN} 87.41 89.41 87.41 87.77 {LBP: KNN, Wavelet: RF} 86.96 89.42 86.96 87.30 {LBP: KNN, Wavelet: SVC} 86.42 88.01 86.42 86.74 {LBP: RF, Wavelet: KNN} 94.51 94.44 94.51 94.45 {LBP: RF, Wavelet: RF} 91.83 92.02 91.83 91.76 {LBP: RF, Wavelet: SVC} 92.67 92.74 92.67 92.66 {LBP: SVC, Wavelet: KNN} 94.58 94.52 94.58 94.54 {LBP: SVC, Wavelet: RF} 91.22 91.25 91.22 91.16 {LBP: SVC, Wavelet: SVC} 92.83 92.77 92.83 92.78 {HOG: KNN, Wavelet: KNN} 94.51 94.67 94.51 94.37 {HOG: KNN, Wavelet: RF} 96.34 96.35 96.34 96.32 {HOG: KNN, Wavelet: SVC} 96.10 96.09 96.10 96.09 {HOG: RF, Wavelet: KNN} 94.81 94.76 94.81 94.76 {HOG: RF, Wavelet: RF} 91.68 91.95 91.68 91.60 {HOG: RF, Wavelet: SVC} 93.44 93.45 93.44 93.40 {HOG: SVC, Wavelet: KNN} 96.11 96.08 96.11 96.08 {HOG: SVC, Wavelet: RF} 94.20 94.30 94.20 94.16 {HOG: SVC, Wavelet: SVC} 94.58 94.51 94.58 94.54 {LBP: KNN, HOG: KNN, Wavelet: KNN} 90.39 91.84 90.39 90.58 {LBP: KNN, HOG: KNN, Wavelet: RF} 90.39 91.47 90.39 90.50 {LBP: KNN, HOG: KNN, Wavelet: SVC} 91.08 92.27 91.08 91.19 {LBP: KNN, HOG: RF, Wavelet: KNN} 89.78 90.85 89.78 89.92 {LBP: KNN, HOG: RF, Wavelet: RF} 87.26 89.60 87.26 87.55 {LBP: KNN, HOG: RF, Wavelet: SVC} 87.26 89.14 87.26 87.56 {LBP: KNN, HOG: SVC, Wavelet: KNN} 90.24 91.41 90.24 90.38 {LBP: KNN, HOG: SVC, Wavelet: RF} 88.33 90.44 88.33 88.61 {LBP: KNN, HOG: SVC, Wavelet: SVC} 88.48 90.27 88.48 88.75 {LBP: RF, HOG: KNN, Wavelet: KNN} 96.03 96.01 96.03 96.01 {LBP: RF, HOG: KNN, Wavelet: RF} 96.11 96.17 96.11 96.10 {LBP: RF, HOG: KNN, Wavelet: SVC} 96.42 96.42 96.42 96.40 {LBP: RF, HOG: RF, Wavelet: KNN} 94.66 94.62 94.66 94.61 {LBP: RF, HOG: RF, Wavelet: RF} 91.69 91.97 91.69 91.60 {LBP: RF, HOG: RF, Wavelet: SVC} 92.75 92.91 92.75 92.71 {LBP: RF, HOG: SVC, Wavelet: KNN} 95.88 95.84 95.88 95.85 {LBP: RF, HOG: SVC, Wavelet: RF} 93.67 93.78 93.67 93.62 {LBP: RF, HOG: SVC, Wavelet: SVC} 93.97 93.98 93.97 93.93 {LBP: SVC, HOG: KNN, Wavelet: KNN} 96.34 96.33 96.34 96.33 {LBP: SVC, HOG: KNN, Wavelet: RF} 95.96 95.97 95.96 95.94 {LBP: SVC, HOG: KNN, Wavelet: SVC} 96.49 96.48 96.49 96.48 {LBP: SVC, HOG: RF, Wavelet: KNN} 94.58 94.53 94.58 94.54 {LBP: SVC, HOG: RF, Wavelet: RF} 91.61 91.78 91.61 91.52 {LBP: SVC, HOG: RF, Wavelet: SVC} 92.98 93.01 92.98 92.93 {LBP: SVC, HOG: SVC, Wavelet: KNN} 95.42 95.38 95.42 95.39 {LBP: SVC, HOG: SVC, Wavelet: RF} 93.52 93.57 93.52 93.47 {LBP: SVC, HOG: SVC, Wavelet: SVC} 94.43 94.44 94.43 94.40 Open in a new tab The comparison of ablation study notes that we achieved a good accuracy of using complementary features and classifiers as can be seen in the Fig 6 . The working procedure is initiated by a test image, on which three features are obtained including LBP, HOG, and Wavelet Transform. These features are handled separately by their particular classifier, i.e., LBP with SVC, HOG with KNN, and Wavelet Transform with SVC. The results of these classifiers are then fused using probabilistic score fusion method in coming up with the final class label which takes a value between 1 to k (where k is the number of classes). The method has the advantage of taking a benefit of advantages of both feature-classifier pair, leading to better accuracy and robustness as is observed in the experimental data. Fig 6. Improved accuracy through feature extraction and classifier fusion using LBP, HOG, and Wavelet Transform. Open in a new tab Individual feature experimental results This part represents an experimental part with emphasis on performance of single features (LBP, HOG, and Wavelet) classification in two more datasets. Several classifiers were used to determine the best features-classifier choices by dataset. Also, the multi-class and binary classification tasks were performed on a three-class dataset and a binary dataset. The performance of various features and models in term of the classification accuracies on Dataset II and Dataset III are summarized in Tables 4 and 5 respectively. The maximum accuracies of each of the datasets are depicted in the tables. Table 4. Individual feature results for Dataset II. Feature Classifier Train Accuracy (%) Test Accuracy (%) Precision (%) Sensitivity (%) F1 Score (%) LBP KNN 60.48 47.80 28.29 47.80 34.91 LBP RF 99.97 82.22 84.65 82.22 82.73 LBP SVC 99.97 89.40 89.59 89.40 89.43 HOG KNN 98.24 95.27 95.24 95.27 95.24 HOG RF 99.97 89.72 90.45 89.72 89.82 HOG SVC 99.97 93.47 93.41 93.47 93.43 Wavelet KNN 94.37 88.74 88.52 88.74 88.58 Wavelet RF 99.97 90.70 91.46 90.70 90.84 Wavelet SVC 99.97 87.44 87.50 87.44 87.45 Open in a new tab Table 5. Individual feature results for Dataset III. Feature Classifier Train Accuracy (%) Test Accuracy (%) Precision (%) Sensitivity (%) F1 Score (%) LBP KNN 97.50 94.83 94.86 94.83 94.83 LBP RF 100.00 90.50 90.74 90.50 90.49 LBP SVC 100.00 95.67 95.67 95.67 95.67 HOG KNN 98.67 98.00 98.00 98.00 98.00 HOG RF 100.00 91.50 91.54 91.50 91.50 HOG SVC 100.00 97.33 97.33 97.33 97.33 Wavelet KNN 97.54 90.33 91.49 90.33 90.27 Wavelet RF 100.00 94.50 94.54 94.50 94.50 Wavelet SVC 100.00 97.67 97.67 97.67 97.67 Open in a new tab The findings show that feature-classifier pairs are different in their performance based on the type of dataset, which gives an idea of a configuration that reaches a higher accuracy rate and robustness. HOG always outperformed other features, and this only highlights its effectiveness in the representation of texture details, which are important in making accurate BTC. Different datasets gave different results regarding classifiers with SVC and KNN being among the most frequently accurate. Probability score fusion experimental results In this section, the performance of the classification is demonstrated by having predictions of several pairs of the features-classifiers combined by employing the newly suggested LHW-Net strategy. This technique significantly increased classification quality on most datasets, which is an indication of the effectiveness of production of the complementary advantages of different features and different classifiers. The results for each dataset are represented in the following figures. Regarding each dataset, the results, obtained in terms of confusion matrices, of both methods using individual sets of features and the fusion technique are presented visually in Figs 7 – 9 . The given matrices offer some comparative analysis, as they depict the differences between various feature extraction techniques and their combination with regard to their performance in relation to the given datasets. Fig 10 shows the result of comparing individual feature-based and fusion results of each dataset. The suggested LHW-Net algorithm is much more accurate on the classification. As two features are combined, the accuracy increases by contrast to single features, and integrating three features brings a slight increase in accuracy. Fig 7. Confusion matrix for Dataset I. Open in a new tab Fig 9. Confusion matrix for Dataset III. Open in a new tab Fig 10. Accuracy comparison of the best individual feature-based models and feature fusion models across datasets. Open in a new tab Fig 8. Confusion matrix for Dataset II. Open in a new tab Table 6 contains the overall comparison in terms of the accuracy, sensitivity, precision and F1 score of the models on the three datasets and allows a more accurate comparison in terms of numerical values. On all the datasets, the suggested model demonstrates better results in all the metrics of evaluation. This proves to show how strong the methodology of fusion is to improve the accuracy and reliability of classification of various datasets. These findings are also supported by the tabular representation as it provides a systematic comparison of the proposed fusion with the improved results in an understandable and clear form. Table 6. Comparison of metrics for individual features, two-feature combination, and three-feature combination. Dataset Feature Type Accuracy (%) Precision (%) Sensitivity (%) F1 Score (%) Dataset I Individual 93.66 93.65 93.66 93.63 Two-Feature Combination 96.34 96.35 96.33 96.33 Three-Feature Combination 96.49 96.48 96.49 96.48 Dataset II Individual 95.27 95.24 95.27 95.24 Two-Feature Combination 96.25 96.26 96.25 96.24 Three-Feature Combination 96.41 96.41 96.41 96.40 Dataset III Individual 98.00 98.00 98.00 98.00 Two-Feature Combination 98.67 98.67 98.67 98.67 Three-Feature Combination 98.67 98.67 98.67 98.67 Open in a new tab In addition, the outstanding performance of the LHW-Net method can also be observed not only in the achievement parameters of accuracy but also in the greater values of precisions, sensitivities and F1 scores in all three datasets. This enhancement proves that the use of complementarity of the strength of specific feature-classifier pairings LBP with SVC, HOG with KNN, and Wavelet with SVC coupled by probabilistic score fusion makes reliable and generalized classification possible. These findings confirm that not only does the fusion strategy reduce the detriments of individual feature sets or classifiers, but it can also allow a more comprehensive decision-making paradigm. As a result, the proposed model has great promise in classifying brain tumors based on work under different data distributions, hence proving its competence to deal with most real-life complex problems with greater accuracy and stability. A comparison of the proposed technique with some of the more established approaches is given in Table 7 . It provides a gamut of approaches that entails hand-made features like Gabor filters, HOG, and DSURF, and deep learning handled approaches like VGG16, ResNet, DenseNet, CNN, and Siamese Networks. Some of the hybrid approaches which incorporate feature extraction algorithms with classifiers such as SVM have also been mentioned. The suggested model is based on the idea of feature-wise combination of LBP, HOG, and Wavelet features with multiple classifiers used to improve the performance. The proposed approach proves to be very robust, scalable, and accurate on all three datasets as indicated by the results. This assessment proves that it is better than the current approaches, which underlines its great usefulness and efficiency. Table 7. Comparison of Classification approaches across all datasets with existing literature. Author Year Dataset (Split) Method Accuracy (%) [ 31 ] 2023 Dataset I Gabor filter and ResNet50 features were extracted from MRI images and classified individually and combined using SVM 95.73 [ 32 ] 2024 Dataset I VGG16, ResNet18, and DenseNet pretrained models were compared for BTC 95.00 [ 33 ] 2023 Dataset I HOG + Machine Learning Classifiers 92.02 [ 34 ] 2026 Dataset I BoT-YOLOv6 96.37 [ 35 ] 2024 Dataset I Combination of SVM, HOG, LBP, and PCA 96.03 [ 36 ] 2024 Dataset I Vision Transformer with self-attention, relative positional encoding, and residual MLP 91.36 Proposed Model Dataset I Combination of Feature Fusion (LBP + SVC, HOG + KNN, Wavelet+SVC) 96.49 [ 1 ] 2022 Dataset II An 8-layer classical CNN-based network was developed for classification 93.83 [ 37 ] 2021 Dataset II Siamese Neural Network 92.60 [ 10 ] 2019 Dataset II A GAN-pretrained deep neural network was fine-tuned to classify tumor classes 93.01(introduced split) [ 38 ] 2022 Dataset II Multi-Channel CNN 89.81 [ 14 ] 2022 Dataset II Normalization, DSURF, and HOG features classified with SVM 90.27 [ 39 ] 2024 Dataset II VGG16 95 Proposed Model Dataset II Combination of Feature Fusion (LBP + SVC, HOG + KNN, Wavelet+SVC) 96.41 [ 40 ] 2023 Dataset III (8:2) CNN optimized using improved Political Optimizer 97.09 [ 41 ] 2023 Dataset III A new CNN architecture for BTC 97.20 [ 1 ] 2023 Dataset III (8:2) An 8-layer classical CNN-based network was developed for classification 97.26 [ 42 ] 2025 Dataset III Optimized Custom-CNN 97.22 Proposed Model Dataset III Combination of Feature Fusion (LBP + SVC, HOG + KNN, Wavelet+SVC) 98.67 Open in a new tab However, some limitations are to be considered in the proposed work. The framework is based on handcrafted features, which might not be able to capture deeper semantic information in MRI images. Also, the model training takes much time, especially the SVC classifier when using large feature sets. Future directions will be to solve these problems through the combination of handcrafted and deep learning-based features, domain adaptation mechanisms to enhance robustness to a variety of datasets, and classifier optimization to achieve accuracy with minimal training complexity. Conclusion The current study focuses on brain tumour classification using a strong framework, which extracts handcrafted features based on LBP, HOG, and Wavelet transforms. These features are then combined by using a selection of machine learning classifiers. A probabilistic score fusion strategy allows combining the results of the individual classifiers and greatly increases the quality of the classification. An experimental study based on standard popular benchmark datasets proves the accuracy and efficiency of the proposed framework. Combining handcrafted features with ensemble strategies is also an area of potential explored in the present work. Supporting information S1 File. Description of the datasets used in this study, with download links. (PDF) pone.0346821.s001.pdf (84.7KB, pdf) Data Availability All the data used in the publication are publicly available in repositories with following URL’s Brain Tumor MRI Dataset: https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset . Brain Tumor Image Dataset https://www.kaggle.com/datasets/denizkavi1/brain-tumor/data . Br35H Brain Tumor Detection 2020 https://www.kaggle.com/datasets/ahmedhamada0/brain-tumor-detection . Funding Statement The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/588/46. References 1. Cinar N, Kaya M, Kaya B. A novel convolutional neural network‐based approach for brain tumor classification using magnetic resonance images. Int J Imaging Syst Tech. 2022;33(3):895–908. doi: 10.1002/ima.22839 [ DOI ] [ Google Scholar ] 2. Kaplan K, Kaya Y, Kuncan M, Ertunç HM. Brain tumor classification using modified local binary patterns (LBP) feature extraction methods. Med Hypotheses. 2020;139:109696. doi: 10.1016/j.mehy.2020.109696 [ DOI ] [ PubMed ] [ Google Scholar ] 3. Deepak S, Ameer PM. Automated Categorization of Brain Tumor from MRI Using CNN features and SVM. J Ambient Intell Human Comput. 2020;12(8):8357–69. doi: 10.1007/s12652-020-02568-w [ DOI ] [ Google Scholar ] 4. Khoramipour S, Gandomkar M, Shakiba M. Enhancement of brain tumor classification from MRI images using multi-path convolutional neural network with SVM classifier. Biomedical Signal Processing and Control. 2024;93:106117. doi: 10.1016/j.bspc.2024.106117 [ DOI ] [ Google Scholar ] 5. Katti G, Ara SA, Shireen A. Magnetic resonance imaging (MRI)–A review. International Journal of Dental Clinics. 2011;3(1):65–70. [ Google Scholar ] 6. Khalil MM, Tremoleda JL, Bayomy TB, Gsell W. Molecular SPECT imaging: an overview. International Journal of Molecular Imaging. 2011;2011(1):796025. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 7. Turkington TG. Introduction to PET instrumentation. J Nucl Med Technol. 2001;29(1):4–11. [ PubMed ] [ Google Scholar ] 8. Mettler FA Jr, Wiest PW, Locken JA, Kelsey CA. CT scanning: patterns of use and dose. J Radiol Prot. 2000;20(4):353–9. doi: 10.1088/0952-4746/20/4/301 [ DOI ] [ PubMed ] [ Google Scholar ] 9. Abiwinanda N, Hanif M, Hesaputra ST, Handayani A, Mengko TR. Brain Tumor Classification Using Convolutional Neural Network. IFMBE Proceedings. Springer Nature Singapore. 2018. 183–9. 10.1007/978-981-10-9035-6_33 [ DOI ] [ Google Scholar ] 10. Ghassemi N, Shoeibi A, Rouhani M. Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images. Biomedical Signal Processing and Control. 2020;57:101678. doi: 10.1016/j.bspc.2019.101678 [ DOI ] [ Google Scholar ] 11. Ullah F, Nadeem M, Abrar M, Amin F, Salam A, Alabrah A, et al. Evolutionary Model for Brain Cancer-Grading and Classification. IEEE Access. 2023;11:126182–94. doi: 10.1109/access.2023.3330919 [ DOI ] [ Google Scholar ] 12. Shafi ASM, Rahman MdB, Anwar T, Halder RS, Kays HME. Classification of brain tumors and auto-immune disease using ensemble learning. Informatics in Medicine Unlocked. 2021;24:100608. doi: 10.1016/j.imu.2021.100608 [ DOI ] [ Google Scholar ] 13. Asiri AA, Khan B, Muhammad F, ur Rahman S, A. Alshamrani HA, Alshamrani KA, et al. Machine Learning-Based Models for Magnetic Resonance Imaging (MRI)-Based Brain Tumor Classification. Intelligent Automation & Soft Computing. 2023;36(1):299–312. doi: 10.32604/iasc.2023.032426 [ DOI ] [ Google Scholar ] 14. Ayadi W, Charfi I, Elhamzi W, Atri M. Brain tumor classification based on hybrid approach. Vis Comput. 2020;38(1):107–17. doi: 10.1007/s00371-020-02005-1 [ DOI ] [ Google Scholar ] 15. Dheepak G, J AC, Vaishali D. Brain tumor classification: a novel approach integrating GLCM, LBP and composite features. Front Oncol. 2024;13:1248452. doi: 10.3389/fonc.2023.1248452 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 16. Kang J, Ullah Z, Gwak J. MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers. Sensors (Basel). 2021;21(6):2222. doi: 10.3390/s21062222 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 17. Kibriya H, Amin R, Alshehri AH, Masood M, Alshamrani SS, Alshehri A. A Novel and Effective Brain Tumor Classification Model Using Deep Feature Fusion and Famous Machine Learning Classifiers. Comput Intell Neurosci. 2022;2022:7897669. doi: 10.1155/2022/7897669 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 18. Sarhan AM. Brain Tumor Classification in Magnetic Resonance Images Using Deep Learning and Wavelet Transform. JBiSE. 2020;13(06):102–12. doi: 10.4236/jbise.2020.136010 [ DOI ] [ Google Scholar ] 19. Ghanavati S, Li J, Liu T, Babyn PS, Doda W, Lampropoulos G. Automatic brain tumor detection in Magnetic Resonance Images. In: 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), 2012. 574–7. 10.1109/isbi.2012.6235613 [ DOI ] [ Google Scholar ] 20. Cheng J. Brain tumor dataset. 2017. https://figshare.com/articles/dataset/brain_tumor_dataset/1512427 21. Bhuvaji S, Kadam A, Bhumkar P, Dedge S, Kanchan S. Brain tumor classification (MRI). https://www.kaggle.com/dsv/1183165 . 2020. 22. Hamada A. Brain tumor detection. 2020. https://www.kaggle.com/datasets/ahmedhamada0/brain-tumor-detection 23. Nickparvar M. Brain Tumor MRI Dataset. 2020. https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset 24. Kavi D. Brain Tumor Data. 2020. https://www.kaggle.com/datasets/denizkavi1/brain-tumor/data 25. Guo Z, Zhang L, Zhang D. A completed modeling of local binary pattern operator for texture classification. IEEE Trans Image Process. 2010;19(6):1657–63. doi: 10.1109/TIP.2010.2044957 [ DOI ] [ PubMed ] [ Google Scholar ] 26. Dalal N, Triggs B. Histograms of Oriented Gradients for Human Detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05). 886–93. 10.1109/cvpr.2005.177 [ DOI ] [ Google Scholar ] 27. Akujuobi CM. Wavelets and wavelet transform systems and their applications. Springer. 2022. [ Google Scholar ] 28. Hearst MA, Dumais ST, Osuna E, Platt J, Scholkopf B. Support vector machines. IEEE Intell Syst Their Appl. 1998;13(4):18–28. doi: 10.1109/5254.708428 [ DOI ] [ Google Scholar ] 29. Peterson L. K-nearest neighbor. Scholarpedia. 2009;4(2):1883. doi: 10.4249/scholarpedia.1883 [ DOI ] [ Google Scholar ] 30. Breiman L. Random Forests. Machine Learning. 2001;45(1):5–32. doi: 10.1023/a:1010933404324 [ DOI ] [ Google Scholar ] 31. Ullah S, Ahmad M, Anwar S, Khattak MI. An intelligent hybrid approach for brain tumor detection. Pak J Eng Technol. 2023;6(1):42–50. doi: 10.51846/vol6iss1pp34-42 [ DOI ] [ Google Scholar ] 32. Kargar Nigjeh M, Ajami H, Mahmud A, Hoque MSU, Umbaugh SE. Comparative analysis of deep learning models for brain tumor classification in MRI images using enhanced preprocessing techniques. In: Applications of Digital Image Processing XLVII, 2024. 5. 10.1117/12.3028318 [ DOI ] [ Google Scholar ] 33. Shilaskar S, Mahajan T, Bhatlawande S, Chaudhari S, Mahajan R, Junnare K. Machine Learning based Brain Tumor Detection and Classification using HOG Feature Descriptor. In: 2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS), 2023. 67–75. 10.1109/icscss57650.2023.10169700 [ DOI ] [ Google Scholar ] 34. Zhang H, Huang S-L, Kuruoglu EE. Classification and recognition of brain tumors from MRI images based on improved YOLOv6. Biomedical Signal Processing and Control. 2026;113:109048. doi: 10.1016/j.bspc.2025.109048 [ DOI ] [ Google Scholar ] 35. Basthikodi M, Chaithrashree M, Ahamed Shafeeq BM, Gurpur AP. Enhancing multiclass brain tumor diagnosis using SVM and innovative feature extraction techniques. Sci Rep. 2024;14(1):26023. doi: 10.1038/s41598-024-77243-7 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 36. Hong S, Wu J, Zhu L, Chen W. Brain tumor classification in VIT-B/16 based on relative position encoding and residual MLP. PLoS One. 2024;19(7):e0298102. doi: 10.1371/journal.pone.0298102 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 37. Deepak S, Ameer PM. Brain tumour classification using siamese neural network and neighbourhood analysis in embedded feature space. Int J Imaging Syst Tech. 2021;31(3):1655–69. doi: 10.1002/ima.22543 [ DOI ] [ Google Scholar ] 38. Kesav N, Jibukumar M. Multi-channel CNN based image classification using SKIP connection and MSVM. International Journal of Computers and Applications. 2022;44(10):981–90. [ Google Scholar ] 39. Hardjodipuro RDR, Sugiyarto MA, Wigati FK, Gunawan AAS, Setiawan KE. Brain Cancer Classification based on T1-Weighted Images Using Deep Learning. In: 2024 IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS), 2024. 1–5. 10.1109/aims61812.2024.10513273 [ DOI ] [ Google Scholar ] 40. Bashkandi AH, Sadoughi K, Aflaki F, Alkhazaleh HA, Mohammadi H, Jimenez G. Combination of political optimizer, particle swarm optimizer, and convolutional neural network for brain tumor detection. Biomedical Signal Processing and Control. 2023;81:104434. doi: 10.1016/j.bspc.2022.104434 [ DOI ] [ Google Scholar ] 41. Mahesha Y. Identification of Brain Tumor Images Using a Novel Machine Learning Model. Lecture Notes in Networks and Systems. Springer Nature Singapore. 2023. 447–57. 10.1007/978-981-99-5166-6_30 [ DOI ] [ Google Scholar ] 42. Chhotray SK, Mishra D, Pati SP, Mishra S. An Optimized Cascaded CNN Approach for Feature Extraction From Brain MRIs for Tumor Classification. IEEE Access. 2025;13:32681–705. doi: 10.1109/access.2025.3543214 [ DOI ] [ Google Scholar ] PLoS One. doi: 10.1371/journal.pone.0346821.r001 Decision Letter 0 Anwar PP Abdul Majeed Anwar PP Abdul Majeed Academic Editor Find articles by Anwar PP Abdul Majeed Author information Copyright and License information Roles Anwar PP Abdul Majeed : Academic Editor © 2026 Anwar P.P. Abdul MajeedAnwar P.P. Abdul Majeed This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PMC Copyright notice 18 Sep 2025 Dear Dr. Rafi, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Nov 02 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at [email protected] . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .... We look forward to receiving your revised manuscript. Kind regards, Anwar P.P. Abdul Majeed Academic Editor PLOS ONE Journal Requirements: When submitting your revision, we need you to address these additional requirements. 1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf 2. Please note that PLOS One has specific guidelines on code sharing for submissions in which author-generated code underpins the findings in the manuscript. In these cases, we expect all author-generated code to be made available without restrictions upon publication of the work. Please review our guidelines at https://journals.plos.org/plosone/s/materials-and-software-sharing#loc-sharing-code and ensure that your code is shared in a way that follows best practice and facilitates reproducibility and reuse. 3. Thank you for stating the following financial disclosure: The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/588/46. Please state what role the funders took in the study. If the funders had no role, please state: "The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript." If this statement is not correct you must amend it as needed. Please include this amended Role of Funder statement in your cover letter; we will change the online submission form on your behalf. 4. When completing the data availability statement of the submission form, you indicated that you will make your data available on acceptance. We strongly recommend all authors decide on a data sharing plan before acceptance, as the process can be lengthy and hold up publication timelines. Please note that, though access restrictions are acceptable now, your entire data will need to be made freely accessible if your manuscript is accepted for publication. This policy applies to all data except where public deposition would breach compliance with the protocol approved by your research ethics board. If you are unable to adhere to our open data policy, please kindly revise your statement to explain your reasoning and we will seek the editor's input on an exemption. Please be assured that, once you have provided your new statement, the assessment of your exemption will not hold up the peer review process. 5. Please upload a new copy of Figures 4 and 6 as the detail is not clear. Please follow the link for more information: https://blogs.plos.org/plos/2019/06/looking-good-tips-for-creating-your-plos-figures-graphics/ 6. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information . 7. If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? Reviewer #1: Partly ********** 2. Has the statistical analysis been performed appropriately and rigorously? -->?> Reviewer #1: Yes ********** 3. Have the authors made all data underlying the findings in their manuscript fully available??> The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.--> Reviewer #1: Yes ********** 4. Is the manuscript presented in an intelligible fashion and written in standard English??> Reviewer #1: Yes ********** Reviewer #1: 1. The quality of the figures is low. Please consider providing the images in higher resolution, as the information in the images is difficult to read. 2. Did the authors split the data into training and testing sets after data augmentation? I am concerned about potential data leakage if this approach was used. 3. Could you double check if this CUDA version is correct: CUDA 12.5555.42.02? 4. The part of "Ablation Study for Base Learner Identification" should be moved to the section of "Experimental Results and Discussion", as it presents the findings of your experiment. 5. The "Performance measures" should be moved to the section of "Materials and Methods", as it describes the evaluation metrics used to measure performance. 6. In Table 7, please specify the classifier that produced the optimal outcomes in your proposed model, rather than writing “Combination of Feature Fusion (LBP, HOG, Wavelet) with Various Classifiers.” 7. I am a bit confused about the design of the proposed model. Is it an ensemble model with three classifiers at the last layer, or is it three individual models, each with one classifier (KNN, RF, SVC)? 8. What are the limitations of the experiment? This should be mentioned in the section of "Experimental Results and Discussion". ********** what does this mean? ). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our Privacy Policy ..--> Reviewer #1: Yes: Yun Xin TeohYun Xin TeohYun Xin TeohYun Xin Teoh ********** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at [email protected] . Please note that Supporting Information files do not need this step.. Please note that Supporting Information files do not need this step.. Please note that Supporting Information files do not need this step.. Please note that Supporting Information files do not need this step. PLoS One. 2026 Apr 21;21(4):e0346821. doi: 10.1371/journal.pone.0346821.r002 Author response to Decision Letter 1 Article notes Copyright and License information Collection date 2026. PMC Copyright notice 22 Oct 2025 Dear Reviewer (s), We sincerely thank you for your time and effort in reviewing our manuscript. Your detailed and constructive feedback has been invaluable in improving the clarity, rigor, and overall quality of the work. We have carefully addressed each of your comments and incorporated your suggestions into the revised version. Reviewer 1: Comment 1: The quality of the figures is low. Please consider providing the images in higher resolution, as the information in the images is difficult to read. Response: The figures have been replaced with more detailed ones (300 dpi) to make them clear and readable. Moreover, the subfigures are eliminated and rearranged as separate figures to present them better. The revised version of the manuscript consists of clearer plots, confusion matrices, and sample images to visualize them better. Location in Paper: Throughout the paper. Comment 2: Did the authors split the data into training and testing sets after data augmentation? I am concerned about potential data leakage if this approach was used. Response: In the revised version, we ensured that no data leakage occurred. For Dataset I, the data was already provided as predefined training and test sets, and only the training set was augmented. For Datasets II and III, the data was first divided into training and test sets, after which only the training sets were augmented. The overall process of data splitting and augmentation has been clearly illustrated in Figures 3 and 5 of the revised manuscript. Location in Paper: Page numbers: 05, Line number 180 and 181. Comment 3: Could you double check if this CUDA version is correct: CUDA 12.5555.42.02? Response: Thank you for your suggestion. The CUDA 12.5 version is supported by the driver, whereas our experiments were based on TensorFlow 2.4.0 and CUDA 11.2, cuDNN 8. Manuscript was updated to eliminate confusion. Location in Paper: Page numbers: 11, Line number 345 to 350. Comment 4: The part of "Ablation Study for Base Learner Identification" should be moved to the section of "Experimental Results and Discussion", as it presents the findings of your experiment. Response: As per your suggestion the manuscript had restructured accordingly. Location in Paper: Page numbers: 11 and 12. Comment 5: The "Performance measures" should be moved to the section of "Materials and Methods", as it describes the evaluation metrics used to measure performance. Response: As per your suggestion the manuscript had restructured accordingly. Location in Paper: Page numbers: 10. Comment 6: In Table 7, please specify the classifier that produced the optimal outcomes in your proposed model, rather than writing “Combination of Feature Fusion (LBP, HOG, Wavelet) with Various Classifiers.” Response: Table 7 has been updated to include a specific reference to the classifier(s) that obtained the best results on each dataset (e.g. LBP+SVC, HOG+KNN, Wavelet+SVC) . Location in Paper: Page numbers: 15. Comment 7: I am a bit confused about the design of the proposed model. Is it an ensemble model with three classifiers at the last layer, or is it three individual models, each with one classifier (KNN, RF, SVC)? Response: In the initial stage, we employed three different classifiers—KNN, RF, and SVC—to train individually on distinct feature sets (LBP, HOG, and Wavelet, respectively). We then evaluated multiple feature–classifier pairings through probabilistic score fusion. The most effective combinations identified were LBP + SVC, HOG + KNN, and Wavelet + SVC, which were subsequently integrated at the decision level. Therefore, the proposed approach does not consist of three independent models but represents a single ensemble framework, wherein feature-specific classifiers contribute to a unified final decision. Location in Paper: This clarification has been added to the revised manuscript on Page 14, Lines 421–424. Comment 8: What are the limitations of the experiment? This should be mentioned in the section of "Experimental Results and Discussion". Response: In the revised manuscript, we have explicitly mentioned the limitations in the “Experimental Results and Discussion” section. The current framework demonstrates promising accuracy; however, it has the following limitations: 1. The proposed model relies on handcrafted feature descriptors, which may not capture deeper semantic representations inherent in complex attack patterns. 2. The approach is computationally intensive, particularly when using the SVC classifier with large feature sets, leading to increased processing time. These limitations have been discussed along with potential directions for improvement in future work. Location in Paper: The corresponding details are provided on Page 14, Lines 442–448 of the revised manuscript. Response to Editorial Requirements: Comment 1: Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. Response: We have modified the manuscript according to the PLOS ONE LaTeX template, to ensure that it is presented according to the style standards of the journal such as file names and formatting. Comment 2: Please note that PLOS One has specific guidelines on code sharing for submissions in which author-generated code underpins the findings in the manuscript. In these cases, we expect all author-generated code to be made available without restrictions upon publication of the work. Response: The author-created code of this study is stored in the following GitHub repository: ( https://github.com/thireesha-suryadevara/LHW-Net ). Comment 3: Thank you for stating the following financial disclosure: The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/588/46. Please state what role the funders took in the study. If the funders had no role, please state: "The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript." If this statement is not correct you must amend it as needed. Please include this amended Role of Funder statement in your cover letter; we will change the online submission form on your behalf. Response: The author Dr. Mudassir Rafi is having dual affiliation as mentioned in the manuscript. Currently, he is associated with the funder, King Khalid University, as an Assistant Professor and as one of the supervisors for the first author, Mrs Thireesha Suryadevara. He is involved in Supervision, reviewing, advising and fund acquisition for the submitted work as stated in the financial disclosure. Comment 4: When completing the data availability statement of the submission form, you indicated that you will make your data available on acceptance. We strongly recommend all authors decide on a data sharing plan before acceptance, as the process can be lengthy and hold up publication timelines. Please note that, though access restrictions are acceptable now, your entire data will need to be made freely accessible if your manuscript is accepted for publication. This policy applies to all data except where public deposition would breach compliance with the protocol approved by your research ethics board. If you are unable to adhere to our open data policy, please kindly revise your statement to explain your reasoning and we will seek the editor's input on an exemption. Please be assured that, once you have provided your new statement, the assessment of your exemption will not hold up the peer review process. Response: We had updated the Data Availability Statement to reflect our intended plan for sharing the data. The data used in this study is publicly available on Kaggle. All code created by the authors (programming code) has been placed in ( https://github.com/thireesha-suryadevara/LHW-Net ) githhub repository. Comment 5: Please upload a new copy of Figures 4 and 6 as the detail is not clear. Response: Figures 4 and 6 are also revised to higher-resolution images (at least 300 dpi) and the subfigures have been replaced to individual full-resolution images, which improves quality and visibility of details. The workflow is represented in figures 3, 5, 7, 8, and 9 of the revised manuscript. Comment 6: Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Response: We included captions for all Supporting Information files at the end of the manuscript, while independently ensuring in-text citations matched the respective files. Comment 7: If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise. Response: We thank the editor for this clarification. Upon reviewing all reviewer comments, we found that no additional citations were specifically recommended for inclusion. Therefore, no new references have been added in this revision. Attachment Submitted filename: Response to Reviewers.docx pone.0346821.s003.docx (23.1KB, docx) PLoS One. doi: 10.1371/journal.pone.0346821.r003 Decision Letter 1 Hikmat Ullah Khan Hikmat Ullah Khan Academic Editor Find articles by Hikmat Ullah Khan Author information Copyright and License information Roles Hikmat Ullah Khan : Academic Editor © 2026 Hikmat Ullah KhanHikmat Ullah Khan This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PMC Copyright notice 20 Feb 2026 Dear Dr. Rafi, [email protected] . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. A letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .... We look forward to receiving your revised manuscript. Kind regards, Hikmat Ullah Khan, PhD (Computer Science) Academic Editor PLOS One Journal Requirements: If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author Reviewer #1: All comments have been addressed Reviewer #2: All comments have been addressed ********** 2. Is the manuscript technically sound, and do the data support the conclusions??> Reviewer #1: Yes Reviewer #2: Yes ********** 3. Has the statistical analysis been performed appropriately and rigorously? -->?> Reviewer #1: Yes Reviewer #2: Yes ********** 4. Have the authors made all data underlying the findings in their manuscript fully available??> The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.--> Reviewer #1: Yes Reviewer #2: Yes ********** 5. Is the manuscript presented in an intelligible fashion and written in standard English??> Reviewer #1: Yes Reviewer #2: Yes ********** Reviewer #1: Thank you for the effort of addressing the comments. However I still have some comments regarding the revised document. - Please don’t use yellow as a highlight color for revisions. It is difficult to read. - For the figures, I still think the quality of images should be enhanced. Reviewer #2: Improve resolution and clarity of all figures and increase font size in diagrams and confusion matrices. Clarify preprocessing steps (resizing, normalization, etc.). Explain hyperparameter tuning strategy. Fix minor typos and formatting inconsistencies. ********** what does this mean? ). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our Privacy Policy ..--> Reviewer #1: Yes: Yun Xin TeohYun Xin TeohYun Xin TeohYun Xin Teoh Reviewer #2: No ********** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] To ensure your figures meet our technical requirements, please review our figure guidelines: https://journals.plos.org/plosone/s/figures You may also use PLOS’s free figure tool, NAAS, to help you prepare publication quality figures: https://journals.plos.org/plosone/s/figures#loc-tools-for-figure-preparation . NAAS will assess whether your figures meet our technical requirements by comparing each figure against our figure specifications. PLoS One. 2026 Apr 21;21(4):e0346821. doi: 10.1371/journal.pone.0346821.r004 Author response to Decision Letter 2 Article notes Copyright and License information Collection date 2026. PMC Copyright notice 10 Mar 2026 Comment 1: Please don’t use yellow as a highlight color for revisions. It is difficult to read. Response: In the revised manuscript, the yellow highlight has been changed to a more readable color (blue) to enhance clarity. Location in Paper: Throughout the paper. Comment 2: For the figures, I still think the quality of images should be enhanced. Response: Thank you for the comment. The figures have been improved in terms of resolution, font sizes, and overall clarity to improve their readability. All figures were checked with NewGen ARTANALYSIS (NAAS tool) in order to match the requirements of the journal in terms of formatting and presentation. Location in Paper: Throughout the paper. Comment 1: Improve resolution and clarity of all figures and increase font size in diagrams and confusion matrices. Response: Every figure has been updated with the improvement of their resolution, the enlargement of fonts in the diagrams and confusion matrices, and the overall clarity to make them easier to read and present better. All figures were checked with NewGen ARTANALYSIS (NAAS tool) in order to match the requirements of the journal in terms of formatting and presentation. Location in Paper: Throughout the paper. Comment 2: Clarify preprocessing steps (resizing, normalization, etc.). Response: We have revised the paper to explain the preprocessing steps, such as Gray scale conversion, resizing etc. Location in Paper: Page number: 05, Line number 154 to 187. Comment 3: Explain hyperparameter tuning strategy. Response: We have provided the description of the hyperparameter tuning strategy in the revised manuscript. The three models, which are KNN, Random Forest, and SVM, were tested on three datasets with varying hyperparameter settings. As the performance varied with different parameter settings, the optimal hyperparameters were empirically determined based on the best overall performance across the datasets. The selected parameters are: KNN (n_neighbors=3), Random Forest, (n_estimators=100), and Linear SVM. Location in Paper: Page number: 08, Line number 274 to 290. Comment 4: Fix minor typos and formatting inconsistencies. Response: The paper has been carefully checked, and all minor typographical errors and formatting discrepancies have been removed. Location in Paper: Throughout the paper. Response to Editorial Requirements: Comment 1: If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise. Response: The reviewer did not make any specific previously published works to be cited. But we have reviewed the literature attentively and included a few recent and relevant references to make the manuscript more powerful and justified. Location in Paper: Page number: 15. Comment 2: Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice. Response: The reference list has been carefully reviewed to make sure that every entry is complete and correct. No retracted articles are mentioned. Moreover, some recent and relevant publications were added to further justify and prove the proposed work. The changes in the reference list have been added to the revised manuscript. Location in Paper: Page number: 15. Attachment Submitted filename: Response_to_Reviewers_auresp_2.docx pone.0346821.s004.docx (12.2KB, docx) PLoS One. doi: 10.1371/journal.pone.0346821.r005 Decision Letter 2 Hikmat Ullah Khan Hikmat Ullah Khan Academic Editor Find articles by Hikmat Ullah Khan Author information Copyright and License information Roles Hikmat Ullah Khan : Academic Editor © 2026 Hikmat Ullah KhanHikmat Ullah Khan This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PMC Copyright notice 24 Mar 2026 LHW-Net: An Ensemble-Based Machine Learning Framework for Brain Tumor Classification PONE-D-25-43376R2 Dear Dr. Rafi, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. For questions related to billing, please contact  and clicking the ‘Update My Information' link at the top of the page. For questions related to billing, please contact  and clicking the ‘Update My Information' link at the top of the page. For questions related to billing, please contact  and clicking the ‘Update My Information' link at the top of the page. For questions related to billing, please contact billing support .... If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact [email protected] . Kind regards, Hikmat Ullah Khan, PhD (Computer Science) Academic Editor PLOS One Additional Editor Comments (optional): Reviewers' comments: Reviewer's Responses to Questions Comments to the Author Reviewer #2: All comments have been addressed ********** 2. Is the manuscript technically sound, and do the data support the conclusions??> Reviewer #2: Yes ********** 3. Has the statistical analysis been performed appropriately and rigorously? -->?> Reviewer #2: Yes ********** 4. Have the authors made all data underlying the findings in their manuscript fully available??> The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.--> Reviewer #2: Yes ********** 5. Is the manuscript presented in an intelligible fashion and written in standard English??> Reviewer #2: Yes ********** Reviewer #2: The manuscript has improved significantly compared with the previous revision, and the authors have addressed the concerns raised in earlier review rounds. I only have a few minor editorial observations: In the abstract, the statement "The classification of brain tumors is an unsolved problem" appears to be a very strong claim and could be softened. The abstract and conclusion are somewhat similar in content. Ideally, the conclusion should briefly highlight the key results or main findings of the study. ********** what does this mean? ). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our Privacy Policy ..--> Reviewer #2: No ********** PLoS One. doi: 10.1371/journal.pone.0346821.r006 Acceptance letter Hikmat Ullah Khan Hikmat Ullah Khan Academic Editor Find articles by Hikmat Ullah Khan Author information Copyright and License information Roles Hikmat Ullah Khan : Academic Editor © 2026 Hikmat Ullah KhanHikmat Ullah Khan This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PMC Copyright notice PONE-D-25-43376R2 PLOS One Dear Dr. Rafi, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS One. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset You will receive further instructions from the production team, including instructions on how to review your proof when it is ready. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few days to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact [email protected] . You will receive an invoice from PLOS for your publication fee after your manuscript has reached the completed accept phase. If you receive an email requesting payment before acceptance or for any other service, this may be a phishing scheme. Learn how to identify phishing emails and protect your accounts at https://explore.plos.org/phishing . If we can help with anything else, please email us at [email protected] . Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Hikmat Ullah Khan Academic Editor PLOS One Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials S1 File. Description of the datasets used in this study, with download links. (PDF) pone.0346821.s001.pdf (84.7KB, pdf) Attachment Submitted filename: Response to Reviewers.docx pone.0346821.s003.docx (23.1KB, docx) Attachment Submitted filename: Response_to_Reviewers_auresp_2.docx pone.0346821.s004.docx (12.2KB, docx) Data Availability Statement All the data used in the publication are publicly available in repositories with following URL’s Brain Tumor MRI Dataset: https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset . Brain Tumor Image Dataset https://www.kaggle.com/datasets/denizkavi1/brain-tumor/data . Br35H Brain Tumor Detection 2020 https://www.kaggle.com/datasets/ahmedhamada0/brain-tumor-detection . Articles from PLOS One are provided here courtesy of PLOS ACTIONS View on publisher site PDF (2.6 MB) Cite Collections Permalink PERMALINK Copy RESOURCES Similar articles Cited by other articles Links to NCBI Databases Cite Copy Download .nbib .nbib Format: AMA APA MLA NLM Add to Collections Create a new collection Add to an existing collection Name your collection * Choose a collection Unable to load your collection due to an error Please try again Add Cancel Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top