Automated Glaucoma Screening and Diagnosis Based on Retinal Fundus Images Using Deep Learning Approaches: A Comprehensive Review

Glaucoma is a chronic eye disease that may lead to permanent vision loss if it is not diagnosed and treated at an early stage. The disease originates from an irregular behavior in the drainage flow of the eye that eventually leads to an increase in intraocular pressure, which in the severe stage of...

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Published in:Diagnostics
Main Author: Zedan M.J.M.; Zulkifley M.A.; Ibrahim A.A.; Moubark A.M.; Kamari N.A.M.; Abdani S.R.
Format: Review
Language:English
Published: Multidisciplinary Digital Publishing Institute (MDPI) 2023
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164927945&doi=10.3390%2fdiagnostics13132180&partnerID=40&md5=9a1297188ca2bc9e9246390ac4554456
id 2-s2.0-85164927945
spelling 2-s2.0-85164927945
Zedan M.J.M.; Zulkifley M.A.; Ibrahim A.A.; Moubark A.M.; Kamari N.A.M.; Abdani S.R.
Automated Glaucoma Screening and Diagnosis Based on Retinal Fundus Images Using Deep Learning Approaches: A Comprehensive Review
2023
Diagnostics
13
13
10.3390/diagnostics13132180
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164927945&doi=10.3390%2fdiagnostics13132180&partnerID=40&md5=9a1297188ca2bc9e9246390ac4554456
Glaucoma is a chronic eye disease that may lead to permanent vision loss if it is not diagnosed and treated at an early stage. The disease originates from an irregular behavior in the drainage flow of the eye that eventually leads to an increase in intraocular pressure, which in the severe stage of the disease deteriorates the optic nerve head and leads to vision loss. Medical follow-ups to observe the retinal area are needed periodically by ophthalmologists, who require an extensive degree of skill and experience to interpret the results appropriately. To improve on this issue, algorithms based on deep learning techniques have been designed to screen and diagnose glaucoma based on retinal fundus image input and to analyze images of the optic nerve and retinal structures. Therefore, the objective of this paper is to provide a systematic analysis of 52 state-of-the-art relevant studies on the screening and diagnosis of glaucoma, which include a particular dataset used in the development of the algorithms, performance metrics, and modalities employed in each article. Furthermore, this review analyzes and evaluates the used methods and compares their strengths and weaknesses in an organized manner. It also explored a wide range of diagnostic procedures, such as image pre-processing, localization, classification, and segmentation. In conclusion, automated glaucoma diagnosis has shown considerable promise when deep learning algorithms are applied. Such algorithms could increase the accuracy and efficiency of glaucoma diagnosis in a better and faster manner. © 2023 by the authors.
Multidisciplinary Digital Publishing Institute (MDPI)
20754418
English
Review
All Open Access; Gold Open Access; Green Open Access
author Zedan M.J.M.; Zulkifley M.A.; Ibrahim A.A.; Moubark A.M.; Kamari N.A.M.; Abdani S.R.
spellingShingle Zedan M.J.M.; Zulkifley M.A.; Ibrahim A.A.; Moubark A.M.; Kamari N.A.M.; Abdani S.R.
Automated Glaucoma Screening and Diagnosis Based on Retinal Fundus Images Using Deep Learning Approaches: A Comprehensive Review
author_facet Zedan M.J.M.; Zulkifley M.A.; Ibrahim A.A.; Moubark A.M.; Kamari N.A.M.; Abdani S.R.
author_sort Zedan M.J.M.; Zulkifley M.A.; Ibrahim A.A.; Moubark A.M.; Kamari N.A.M.; Abdani S.R.
title Automated Glaucoma Screening and Diagnosis Based on Retinal Fundus Images Using Deep Learning Approaches: A Comprehensive Review
title_short Automated Glaucoma Screening and Diagnosis Based on Retinal Fundus Images Using Deep Learning Approaches: A Comprehensive Review
title_full Automated Glaucoma Screening and Diagnosis Based on Retinal Fundus Images Using Deep Learning Approaches: A Comprehensive Review
title_fullStr Automated Glaucoma Screening and Diagnosis Based on Retinal Fundus Images Using Deep Learning Approaches: A Comprehensive Review
title_full_unstemmed Automated Glaucoma Screening and Diagnosis Based on Retinal Fundus Images Using Deep Learning Approaches: A Comprehensive Review
title_sort Automated Glaucoma Screening and Diagnosis Based on Retinal Fundus Images Using Deep Learning Approaches: A Comprehensive Review
publishDate 2023
container_title Diagnostics
container_volume 13
container_issue 13
doi_str_mv 10.3390/diagnostics13132180
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164927945&doi=10.3390%2fdiagnostics13132180&partnerID=40&md5=9a1297188ca2bc9e9246390ac4554456
description Glaucoma is a chronic eye disease that may lead to permanent vision loss if it is not diagnosed and treated at an early stage. The disease originates from an irregular behavior in the drainage flow of the eye that eventually leads to an increase in intraocular pressure, which in the severe stage of the disease deteriorates the optic nerve head and leads to vision loss. Medical follow-ups to observe the retinal area are needed periodically by ophthalmologists, who require an extensive degree of skill and experience to interpret the results appropriately. To improve on this issue, algorithms based on deep learning techniques have been designed to screen and diagnose glaucoma based on retinal fundus image input and to analyze images of the optic nerve and retinal structures. Therefore, the objective of this paper is to provide a systematic analysis of 52 state-of-the-art relevant studies on the screening and diagnosis of glaucoma, which include a particular dataset used in the development of the algorithms, performance metrics, and modalities employed in each article. Furthermore, this review analyzes and evaluates the used methods and compares their strengths and weaknesses in an organized manner. It also explored a wide range of diagnostic procedures, such as image pre-processing, localization, classification, and segmentation. In conclusion, automated glaucoma diagnosis has shown considerable promise when deep learning algorithms are applied. Such algorithms could increase the accuracy and efficiency of glaucoma diagnosis in a better and faster manner. © 2023 by the authors.
publisher Multidisciplinary Digital Publishing Institute (MDPI)
issn 20754418
language English
format Review
accesstype All Open Access; Gold Open Access; Green Open Access
record_format scopus
collection Scopus
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