(NHCE, 2023) Diwakar R V 1NH20IS046 Sravan Kumar T 1NH20IS181 B Jaffer Sadiq 1NH20IS194
Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide. Early detectionand accurate
diagnosis of CVDs are crucial for effective intervention and improved patientoutcomes. Retinal imaging
has emerged as a non-invasive and cost-effective technique for CVD prediction. This study aims to
develop a deep learning model using convolutionalneural networks (CNNs) and MobileNet architecture
to predict CVDs from retinal images. The proposed model leverages the capabilities of CNNs to
automatically learn relevant featuresfromretinal images and MobileNet'slightweight design for efficient
deployment.A large dataset ofretinal images, including healthy individuals and CVD patients, is utilizedfor
model training and evaluation.