Prediction of Cardiovascular Diseases with Retinal Images Using Deep Learning

Authors- Professor Dr. G.Ramasubba Reddy, Yakasi Vasanthi, Panga Rupa, Shaik Basid Ahammad, Shaik Mulkisabgari Mahaboob Basha

Abstract-Cardiovascular diseases (CVDs) are among the leading causes of death worldwide, and early detection plays a crucial role in improving patient outcomes. Recent advancements in medical imaging, particularly retinal imaging, have opened new possibilities for identifying cardiovascular risk factors. The retina, with its direct connection to the central nervous system and vascular network, reflects the condition of systemic blood vessels, making retinal images a valuable tool for assessing cardiovascular health. This study explores the use of deep learning techniques, particularly Convolutional Neural Networks (CNNs), to predict cardiovascular diseases from retinal images. The approach involves preprocessing retinal images through normalization, data augmentation, and segmentation, followed by the application of deep learning models for classification. The models are trained to identify key features such as blood vessel abnormalities, microaneurysms, and optic disc changes that correlate with CVD risk. Transfer learning and multimodal approaches, combining retinal images with clinical data, are also explored to enhance prediction accuracy. The results demonstrate that deep learning models, with their ability to automatically extract complex patterns from retinal images, offer significant potential for non-invasive, early detection of cardiovascular diseases. Challenges such as data imbalance, model interpretability, and the need for large annotated datasets are discussed. Overall, this study highlights the promising role of deep learning in revolutionizing cardiovascular disease prediction through retinal imaging, offering a novel approach for preventive healthcare.

DOI: /10.61463/ijset.vol.13.issue1.181