NCBI PubMed Central
open access
An interpretable machine learning model for MRI-based morphological classification of lumbar disc herniation using functional biomechanical features.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Performance analysis of automatic techniques for tissue classification in magnetic resonance images of the human brain#1020402
- Magnetic Resonance Imaging-based Prostate Cancer Diagnosis Using Principal Component Analysis and Machine Learning Algorithms.#354738
- Classification of spinal tuberculous infection, pyogenic infection and spinal metastasis from magnetic resonance imaging using machine learning.#242255
- Machine learning-based integration of transcriptome and digital pathology for predicting chemoresistance in muscle-invasive bladder cancer.#262998
- Influence of Functional Magnetic Resonance Imaging Data Preprocessing Pipelines on the Accuracy of Schizophrenia Classification Using Machine Learning Methods.#412104
Record · ID 1404033
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive