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. Attached Files Name Description MIMEType Size Downloads khan-areviewonglaucomadisease-2021.pdf Published version application/pdf 7.01MB 13 Title A Review on Glaucoma Disease Detection Using Computerized Techniques Author(s) Abdullah, F Imtiaz, R Madni, HA Khan, HA 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 Persistent URL http://hdl.handle.net/10536/DRO/DU:30149238 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment Open Access Collection Related Links Link Description Link to full-text (open access)     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. 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. Versions Version Filter Type Wed, 17 Mar 2021, 07:22:42 EST Wed, 17 Mar 2021, 09:51:17 EST Thu, 18 Mar 2021, 04:01:30 EST Thu, 18 Mar 2021, 05:05:05 EST Sat, 20 Mar 2021, 01:36:05 EST Wed, 24 Mar 2021, 10:41:02 EST Wed, 24 Mar 2021, 10:42:26 EST Filtered Full Citation counts:   Cited 1 times in TR Web of Science Cited 1 times in Scopus Search Google Scholar Access Statistics: 18 Abstract Views, 13 File Downloads  -  Detailed Statistics Created: Wed, 17 Mar 2021, 09:51:17 EST

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