Prediction Of Cardiovascular Disease from retinal images using Deep Learning

Cardio Vascular Diseases (CVDs) remain a leading global health concern, necessitatingearly detection and precise risk assessment to improve patient outcomes. This paper introduces an innovative solution, "Retina Blood Vessel Segmentation using U-Net and Deep learning techniques," for evaluating various cardiovascular diseases non-invasively through the analysis of retinal blood vessels. This paper leverages cutting-edge computer vision techniques to segment retinal vessels from medical images, employing the U-Net architecture for initial segmentation. Deep learning methods, trained on the extracted vessel features, differentiates between normal vascular patterns and those indicative of a range of cardiovascular diseases. This innovative approach enables efficient cardiovascular risk assessment without invasive procedures, addressing the pressing need for non-invasive diagnostic methods. Key benefits and outcomes include early disease detection, non-invasive cardiovascular risk assessment, enhanced diagnostic efficiency, and adaptability to evaluate diverse cardiovascular conditions and risk factors. By contributing a versatile and reliable tool for retinal vessel analysis, this project has the potential to advance medical image analysis and significantly impact cardiovascular disease management.