Alzheimer’s Disease Detection from Retinal Images Using Machine Learning and Deep Learning Techniques: A Perspective
Source abstract
Abstract
Alzheimer’s disease (AD) is a neurodegenerative disease that results in a loss of cognitive functions. The early discovery of it can potentially stop or decrease the severity of AD. Extensive research has been conducted to find AD biomarkers. In recent years, due to the development of AI technologies and the ease of obtaining retinal images, various machine learning (ML)- and deep learning (DL)-based methods of identifying AD patients from these images have been proposed. These models are significant as they represent a potential screening tool for AD and a tool for identifying biomarkers from retinal images. This paper reviews the recent progress in this direction. It presents an overview of relevant methods and analyzes their strengths and limitations. Also, it discusses common challenges and possible future directions related to this topic.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Retinal Imaging and Analysis · Digital Imaging for Blood Diseases · Artificial Intelligence in Healthcare
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR, China
Funding
City University of Hong Kong
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