Spinal Stenosis Detection Through Medical Vision Using Neural Network Driven Techniques

This paper presents “Spinal Stenosis Detection through Medical Vision using Neural Network driven techniques” a system that utilizes Convolution Neural Networks (CNNs) of deep learning algorithms implementing the proposed methodology ResNet, VGG Net and Inception Net to enhance the identification and classification of spinal stenosis in medical images. Spinal stenosis, a condition involving the narrowing of spaces within the spine, requires precise detection for effective intervention. By leveraging CNN architectures, the proposed solution improves the stenosis detection, enabling it to identify intricate features that traditional diagnostic methods may overlook. The research paper is implemented with Django, a web framework that provides an efficient and user-friendly interface for clinicians, human healthcare and researchers. Django's integration allows for seamless data management and streamlined model training, making the system highly accessible for clinical use. This combination of CNN-based medical image analysis and web capabilities provides a powerful tool for medical professionals, supporting enhanced diagnostic accuracy of Res Net is 90%, VGG Net is 92% and Inception Net with high accuracy of 96% and improved Patient outcomes. The proposed approach demonstrates the potential of deep learning model in medical diagnostic, offering a comprehensive, efficient, and robust solution for spinal health management.