Automated Lumbar Spine Degenerative Classification Using Deep Learning: A Comprehensive Evaluation Based on RSNA 2024

Abstract

Degenerative lumbar spine diseases, including disk herniation, spinal stenosis, and facet arthropathy, are leading causes of chronic lower back pain, significantly affecting patients worldwide. Diagnosing these conditions through MRI imaging is time-consuming and subjective. This study introduces a novel deep-learning model using the MobileNetV2-UNet architecture to automate the classification and segmentation of lumbar spine degenerative diseases. The MobileNetV2-UNet model integrates MobileNetV2 for efficient feature extraction and UNet for accurate segmentation. The model was trained and validated on the RSNA 2024 Lumbar Spine Degenerative Classification dataset, which includes labeled MRI images. Depthwise separable convolutions enhanced computational efficiency, while skip connections in the UNet architecture preserved spatial details. The combined Cross-Entropy and IoU loss functions optimized classification accuracy and segmentation quality. The model’s performance was evaluated using 5-fold cross-validation. The MobileNetV2-UNet model achieved a 94.93% accuracy, a 94.61% Dice Coefficient, and a 95.65% F1 score in classifying and segmenting various lumbar spine conditions, such as neural foraminal narrowing and spinal canal stenosis. The model’s efficient architecture enables real-time application in clinical settings while maintaining high performance across diverse datasets. The proposed MobileNetV2-UNet model offers a reliable, efficient solution for automating lumbar spine degenerative disease diagnosis, reducing manual workload, and improving diagnostic precision. Future work will focus on enhancing generalization, improving model interpretability, and applying the framework to other musculoskeletal and neurological conditions, making it a promising tool for clinical use.