In:
Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering), Bentham Science Publishers Ltd., Vol. 16, No. 8 ( 2023-12), p. 790-804
Abstract:
In recent years, the extraction of retinal blood vessels from low contrast retinal images
has become a challenging task for diagnosing retinal diseases such as Diabetic Retinopathy, Agerelated Macular Degeneration (AMD), Retinopathy of Prematurity (ROP), cataract, and glaucoma.
Another challenge is screening the retinal image to identify the disease early on. However, data analysis from a large population-based study of retinal diseases is required to help resolve the uncertainty
in identifying the retinal disease based on retinal image classification using deep learning approaches from the retinal diseases dataset. Therefore, we proposed the survey on the deep learning
approach for screening the retinal image to identify the early stages of the disease and discussed retinal disease analysis based on deep learning approaches to detect Diabetic Retinopathy, AMD
ROP, and Glaucoma. We also discuss deep learning applications in the segmentation of retinal blood vessels, extraction of the optic disc, optic cup, and fovea, and OCT segmentation to detect
retinal disease for diagnosis of diseases. Finally, discuss the classification of arteries/veins using a deep learning approach.
Type of Medium:
Online Resource
ISSN:
2352-0965
DOI:
10.2174/2352096516666221124111107
Language:
English
Publisher:
Bentham Science Publishers Ltd.
Publication Date:
2023
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