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A Review on Glaucoma Disease Detection Using Computerized Techniques

Abdullah, F, Imtiaz, R, Madni, HA, Khan, HA, Khan, T, Khan, MAU and Naqvi, SS 2021, A Review on Glaucoma Disease Detection Using Computerized Techniques, IEEE Access, vol. 9, pp. 37311-37333, doi: 10.1109/access.2021.3061451.

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Title A Review on Glaucoma Disease Detection Using Computerized Techniques
Author(s) Abdullah, F
Imtiaz, R
Madni, HA
Khan, HA
Khan, TORCID iD for Khan, T orcid.org/0000-0002-7477-1591
Khan, MAU
Naqvi, SS
Journal name IEEE Access
Volume number 9
Start page 37311
End page 37333
Total pages 23
Publisher IEEE
Place of publication Piscataway, NJ
Publication date 2021-02-23
ISSN 2169-3536
Keyword(s) glaucoma
convolutional neural networks (CNN)
diabetic retinopathy
cup-to-disc ratio (CDR)
optic nerve head (ONH)
optic cup (OC)
optic disc (OD)
intra ocular pressure (IOP)
Science & Technology
Technology
Computer Science, Information Systems
Engineering, Electrical & Electronic
Telecommunications
Computer Science
Engineering
Retina
Optical imaging
Biomedical optical imaging
Image resolution
Image segmentation
Adaptive optics
Optical variables measurement
OPTIC DISC SEGMENTATION
RETINAL FUNDUS IMAGE
AUTOMATED DETECTION
CUP SEGMENTATION
VESSEL SEGMENTATION
NERVE HEAD
DIAGNOSIS
ATROPHY
PREVALENCE
RATIO
Summary Glaucoma is an incurable eye disease that leads to slow progressive degeneration of the retina. It cannot be fully cured, however, its progression can be controlled in case of early diagnosis. Unfortunately, due to the absence of clear symptoms during the early stages, early diagnosis are rare. Glaucoma must be detected at early stages since late diagnosis can lead to permanent vision loss. Glaucoma affects the retina by damaging the Optic Nerve Head (ONH). Its diagnosis is dependent on the measurements of Optic Cup (OC) and Optic Disc (OD) in the retina. Computer vision techniques have been shown to diagnose glaucoma effectively and correctly with little overhead. These techniques measure OC and OC dimensions using machine learning based classification and segmentation algorithms. This article aims to provide a comprehensive overview of various existing techniques that use machine learning to detect and diagnose glaucoma based on fundus images. Readers would be able to understand the challenges glaucoma presents from an image processing and machine learning stand-point and will be able to identify gaps in current research.
Language eng
DOI 10.1109/access.2021.3061451
Indigenous content off
Field of Research 08 Information and Computing Sciences
09 Engineering
10 Technology
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30149238

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Created: Wed, 17 Mar 2021, 09:51:17 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.