Advancing Lumbar Disc Herniation Classification from MRI Using an Efficient and Reproducible Classical Machine Learning Framework

Background/Objectives: A common diagnosis for lumbar disc herniation is an image taken with Magnetic Resonance Imaging (MRI). Due to the need for expertise during manual interpretation of MRI scans by specialists, the process is very time consuming. The use of a machine learning (ML)-based pathway to classify lumbar MRI images as either “normal” or “herniated” represents an efficient way to analyse MRI scans seeking similar pathologies. Methods: The proposed methodology transforms original lumbar MRIs (from colour to greyscale), resizes them, filters, and normalises them (using bilateral algorithms) before conducting extraction of features using PCA-based decomposition. A total of ten classical ML classifiers were evaluated using five-fold outer and three-fold inner nested cross-validation, with the number of PCA components selected exclusively within the inner folds. Results: Extra Trees achieved the highest F1-score of 85.86% (95% CI: 84.16–87.44%) and the highest ROC-AUC of 90.96% (95% CI: 89.27–92.54%), with an accuracy of 84.29%, precision of 82.03%, recall of 90.07%, and specificity of 77.79%. Random Forest achieved the highest accuracy of 84.36% (95% CI: 82.40–86.26%), although its overall performance was comparable to that of Extra Trees. Conclusions: The findings support the potential of traditional machine learning classifiers for binary classification within the evaluated image-level dataset, while maintaining a relatively simple design and computational efficiency. No direct empirical comparison with deep learning models was conducted in this study.