Authors - Anup Bhitre, Saurabh Nimje, Utkarsha Pacharaney, K. T. Reddy Abstract - Cervical Spinal Stenosis (CSS) is a progressive spinal disorder caused by narrowing of the spinal canal in the neck, potentially leading to severe neurological damage if undiagnosed. Due to rising CSS cases and the limitations of manual MRI analysis—such as subjectivity, time consumption, and inter-observer variation—there is a growing need for automated, reliable diagnostic tools. This study evaluates and compares four AI models—CNN, ResNet50, SVM, and Random Forest—using 1,200 T2-weighted MRI images processed through normalization, segmentation, and augmentation. Performance was measured using accuracy, precision, recall, F1-score, and AUC-ROC. ResNet50 achieved the highest accuracy (93.6%) and AUC-ROC (0.97), demonstrating superior diagnostic performance. SHAP was used for interpretability, highlighting spinal canal diameter and ligamentum flavum thickening as key diagnostic features. The findings confirm that deep learning, especially ResNet50, offers a scalable, interpretable, and clinically effective method for early CSS detection.
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