Abstract:

Disc bulge occurs when the inner component of the intervertebral disc protrudes from its outer wall and progresses over time, which can lead to additional disc degeneration problems such as spinal stenosis and sciatica. Serious bulges on the disc can put pressure on the surrounding nerve roots, causing pain to travel down the back and other parts of the body. The dataset used comprises 515 patients who reported lower back pain. It includes the last 3 lumbar spine discs, D3(L3-L4), D4(L4-L5), and D5(L5- S1) for each of the patients. In this paper, a convolutional neural network (CNN) model has been built to diagnose composite axial MRI scans. The model detects serious disc bulge in these scans and has achieved remarkable accuracy, recall, precision and F1-score of 89%. Local Interpretable Model- Agnostic Explanations (LIME) was applied to explain the model’s decision and hence eliminate the black box problem of the model. This ensures the model provides interpretable insights, making it not only accurate but also reliable. The findings highlight how advanced computational methods can improve medical imaging and transform lumbar spine diagnosis and treatment.