Year: 2025
Type: article
Abstract: : Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide. Early detection and accurate diagnosis of CVDs are crucial for effective intervention and improved patient outcomes. Retinal imaging has emergedasanoninvasiveandcost-effectivetechniquefor CVD prediction. This study aims to develop a deep learning model using convolutional neural networks (CNNs)andMobileNetarchitecturetopredictCVDsfrom retinal images. The proposed model leverages the capabilities of CNNs to automatically learn relevant features from retinal images and MobileNet's lightweight design for efficient deployment
Author: Padakanti Pranathi
Language: English
Topic: Retinal Imaging and Analysis
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4410929189