Spinal Vision CNN-Driven Techniques for Accurate Stenosis Detection

The need for accurate and efficient detection method of spinal stenosis, a condition that involves the narrowing of the spaces in the spine, so as to avoid serious complications. In this project, SPINAL VISION, we present a deep learn based model with Convolutional Neural Nets (CNNs) to improve spinal stenosis detection accuracy. Based on the advanced image preprocessing and image classification techniques, the system achieves high diagnostic precision and sensitivity for reliable identification of stenotic regions within spinal imaging. This proposed methodology is faster and more accurate than traditional diagnostics leading to early intervention and better patient outcomes. Future work will integrate multimodal data and further investigate the interpretability of models for clinical applicability.