A Multi-Model Deep Learning Framework for Automated Lumbar Disease Diagnosis

To improve patient outcomes and lessen the long-term cost on healthcare systems, lumbar illnesses must be accurately diagnosed and classified. Conventional diagnostic methods rely significantly on the knowledge of radiologists, which may result in irregularities and treatment delays. This article presents a novel multi-model deep learning framework that combines the advantages of several CNN architectures, such as MobileNet, DenseNet, ResNet50, and AlexNet, with a CNN-SVM hybrid and an Involutional VGG model, in order to handle this difficulty. The ability to extract and categorize intricate and delicate lumbar spine features from medical images is much improved by this ensemble technique. The diagnosis process is further made more transparent and reliable for doctors by including Gradient-weighted Class Activation Mapping (GRAD-CAM), which offers visual interpretability. A large, annotated dataset of lumbar MRI/X-ray images was used to test the suggested framework. The experimental findings demonstrate better accuracy, recall, precision, and AUC_ROC compared to single-model CNNS. This approach shows promise as a trustworthy decision-support tool for the diagnosis and tracking of lumbar illness in real time.