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AuthorSebastian, Anila
AuthorElharrouss, Omar
AuthorAl-Maadeed, Somaya
AuthorAlmaadeed, Noor
Available date2023-02-23T09:13:03Z
Publication Date2023
Publication NameDiagnostics
ResourceScopus
URIhttp://dx.doi.org/10.3390/diagnostics13030345
URIhttp://hdl.handle.net/10576/40324
AbstractThe number of people who suffer from diabetes in the world has been considerably increasing recently. It affects people of all ages. People who have had diabetes for a long time are affected by a condition called Diabetic Retinopathy (DR), which damages the eyes. Automatic detection using new technologies for early detection can help avoid complications such as the loss of vision. Currently, with the development of Artificial Intelligence (AI) techniques, especially Deep Learning (DL), DL-based methods are widely preferred for developing DR detection systems. For this purpose, this study surveyed the existing literature on diabetic retinopathy diagnoses from fundus images using deep learning and provides a brief description of the current DL techniques that are used by researchers in this field. After that, this study lists some of the commonly used datasets. This is followed by a performance comparison of these reviewed methods with respect to some commonly used metrics in computer vision tasks.
SponsorThis publication was supported by Qatar University Internal Grant QUHI-CENG-22/23-548. The findings achieved herein are solely the responsibility of the authors.
Languageen
PublisherMDPI
Subjectconvolutional neural network
deep learning
diabetic retinopathy detection
diabetic retinopathy grading
retinal fundus images
TitleA Survey on Deep-Learning-Based Diabetic Retinopathy Classification
TypeArticle Review
Pagination-
Issue Number3
Volume Number13
dc.accessType Open Access


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