A Hybrid CNN-SVM Framework for Improved Lumbar Spine Degeneration and Disc Herniation Classification

Lumbar spine degeneration and disc herniation are common and pressing health challenges worldwide, causing chronic lower back pain and significantly impacting patient well-being. Timely and accurate diagnosis of these conditions is critical for effective treatment, as well as for reducing the economic burden on healthcare systems. This study introduces a pioneering approach for automated lumbar disease classification, leveraging advanced deep learning architectures to enhance diagnostic accuracy and efficiency. The proposed methodology integrates state-of-the-art models, including MobileNet, DenseNet, and hybrid CNN-SVM frameworks, each contributing unique strengths in feature extraction and classification. Additionally, an Involutional Neural Network-based VGG architecture is employed to improve the system's ability to analyze intricate and detailed features of lumbar conditions. By training these models on extensive annotated MRI datasets, the system achieves remarkable classification accuracy and computational efficiency, outperforming traditional CNN-based approaches. Cutting-edge segmentation techniques further refine the models' precision, enabling accurate detection and classification. This innovative system holds promise as a robust tool for real-time diagnosis and monitoring in healthcare settings. By providing clinicians with efficient and reliable diagnostic assistance, it has the potential to revolutionize spinal healthcare, improve patient outcomes, and streamline clinical workflows. This research highlights the transformative impact of AI-driven solutions in addressing complex medical challenges and advancing the field of automated medical imaging.