Automated Lumbar Disc Intensity Classification From MRI Scans Using Region-Based CNNs and Transformer Models

Abstract

This study explores the effectiveness of deep learning methodologies in the detection and classification of lumbar disc intensity using MRI scans. Initially, region-based deep learning frameworks, including Faster R-CNN and Mask R-CNN with different backbones such as ResNet50 and ResNet101 are evaluated. Results demonstrated that backbone selection significantly impacts model performance, with Mask R-CNN combined with ResNet101 achieving a remarkable mAP@0.50 (AP50) of 99.83%. In addition to object detection models, Transformer-based classification architectures, including MaxViT, Vision Transformer (ViT), a Hybrid CNN-ViT model, and Fine-Tuned Enhanced Pyramid Network (FT-EPN), are implemented. Among these, the Hybrid model achieved the highest classification accuracy (83.1%), while MaxViT yielded the highest precision (0.804). Comparative analyses highlighted that while Mask R-CNN models excelled in segmentation and detection tasks, Transformer-based models provided effective solutions for direct severity classification of lumbar discs. These findings emphasize the critical role of both backbone architecture and model type in optimizing diagnostic performance. The study demonstrates the potential of integrating region-based and Transformer-based models in advancing automated lumbar spine assessment, paving the way for more accurate and reliable medical diagnostic systems.