Prediction of Alzheimer’s Disease with Retinal Images Using Deep Learning

Abstract

Alzheimer's disease (AD) is a leading cause of mortality worldwide. Early detection and accurate diagnosis of AD are crucial for effective intervention and improved patient outcomes. Retinal imaging has emerged as a non-invasive and cost-effective technique for AD prediction. This study aims to develop a deep learning model using convolutional neural networks (CNNs) architecture to predict AD from retinal images. The proposed model leverages the capabilities of CNNs to automatically learn relevant features from retinal images.