Published Paper Details:

The Intervertebral disc degeneration is a leading cause of spinal disorders, often resulting in chronic back pain and impaired mobility. Accurate and early classification of disc degeneration is critical for timely diagnosis and treatment planning. In this study, we propose a deep learning-based approach utilizing Convolutional Neural Networks (CNNs) to automate the classification of spinal disc degeneration from magnetic resonance imaging (MRI) scans. Our model is trained on a labeled dataset annotated by expert radiologists based on standard grading systems such as the Pfirrmann classification. The proposed CNN architecture is designed to capture complex spatial features and subtle texture variations associated with different stages of disc degeneration. Experimental results demonstrate high classification accuracy, precision, and recall, outperforming traditional image processing and machine learning techniques. This automated system has the potential to assist radiologists in clinical decision-making, reduce diagnostic variability, and improve patient outcomes.