Comprehensive Diagnosis of Cardiovascular Diseases Through Retinal Image Analysis Techniques

Author : Haniya T

Abstract :Cardiovascular diseases (CVDs) remain a leading cause of mortality worldwide, necessitating early prediction methods to mitigate their impact. Retinal imaging provides a non-invasive approach for evaluating cardiovascular health, as retinal vascular features are reflective of systemic conditions. This study leverages advanced convolutional neural networks (CNNs) to analyze retinal images, extracting critical biomarkers such as vessel density and tortuosity to predict CVD risks. By integrating large-scale datasets and optimizing deep learning models, the proposed approach demonstrates high accuracy and robustness in identifying individuals at risk. The findings highlight retinal imaging’s potential as a cost-effective and accessible tool for large-scale CVD screening and early diagnosis, particularly in resource- constrained settings.

Keywords :Cardiovascular diseases, retinal imaging, convolutional neural networks, machine learning, biomarkers, non-invasive diagnostics.

Conference Name :National Conference on Researches in Science and Technology (NCRST-25)

Conference Place Chennai India

Conference Date 10th May 2025