Detection and Classification of Lumbar Abnormalities Using CNN Models

The objective of the SpineAI project is to develop an advanced medical imaging platform that utilizes deep learning techniques for automated lumbar spine analysis. The primary goal is to create a system that accurately detects, segments, and classifies degenerative conditions in the lumbar spine from MRI scans. By integrating state-of-the-art architectures like YOLO, Faster R-CNN, U-Net, and DenseNet, the platform aims to provide precise assessments of spinal abnormalities such as disc degeneration, fractures, and other structural changes. The system’s ability to classify the severity of degenerative conditions, ranging from mild to severe, offers clinicians a valuable tool for making informed decisions about treatment plans. SpineAI seeks to enhance diagnostic accuracy, reduce manual review time, and improve overall patient care by automating the image analysis process. Ultimately, the project aims to revolutionize the way spinal disorders are diagnosed and managed in clinical settings, fostering better patient outcomes.