“Visionary Diagnosis: Exploring Cardiovascular Links in Retinal Imagery”
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Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
NHCE
Abstract
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.