Deep Learning in Oncology: CNN based Lung Cancer Detection using CT Images

The proposed system leverages Convolutional Neural Network (CNN) technologies to perform early lung cancer detection through the analysis of chest CT scan images. Designed as an AI-powered diagnostic aid, the system operates independently of human interpretation, enhancing speed and reliability in medical imaging evaluation. Built using Python-based technologies, the system utilizes TensorFlow and Keras frameworks for deep learning model development and training, supported by OpenCV for image pre-processing and visualization. The system processes CT scan slices to identify pulmonary nodules and classifies them into benign or malignant categories using a trained CNN model. The detection pipeline includes grayscale normalization, image resizing, and segmentation procedures to enhance the visibility of lung tissues. The CNN model extracts spatial hierarchies of features through multiple convolutional and pooling layers, followed by fully connected layers for final classification. In cases where the system detects potential malignancy, it generates a detailed report including confidence scores, prediction labels, and scan timestamps. To maintain responsiveness and scalability, prediction requests are managed through concurrent processing threads, ensuring timely feedback even when multiple scans are evaluated in sequence. An alert throttling mechanism ensures the reduction of false positives by incorporating prediction stability checks over multiple frames or scans.