Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142137
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dc.contributor.authorHagiwara, Yukien_US
dc.contributor.authorKoh, Joel En Weien_US
dc.contributor.authorTan, Jen Hongen_US
dc.contributor.authorBhandary, Sulatha V.en_US
dc.contributor.authorLaude, Augustinusen_US
dc.contributor.authorCiaccio, Edward J.en_US
dc.contributor.authorTong, Louisen_US
dc.contributor.authorAcharya, U. Rajendraen_US
dc.date.accessioned2020-06-16T05:41:00Z-
dc.date.available2020-06-16T05:41:00Z-
dc.date.issued2018-
dc.identifier.citationHagiwara, Y., Koh, J. E. W., Tan, J. H., Bhandary, S. V., Laude, A., Ciaccio, E. J., . . . Acharya, U. R. (2018). Computer-aided diagnosis of glaucoma using fundus images : a review. Computer methods and programs in biomedicine, 165, 1-12. doi:10.1016/j.cmpb.2018.07.012en_US
dc.identifier.issn0169-2607en_US
dc.identifier.urihttps://hdl.handle.net/10356/142137-
dc.description.abstractBackground and objectives: Glaucoma is an eye condition which leads to permanent blindness when the disease progresses to an advanced stage. It occurs due to inappropriate intraocular pressure within the eye, resulting in damage to the optic nerve. Glaucoma does not exhibit any symptoms in its nascent stage and thus, it is important to diagnose early to prevent blindness. Fundus photography is widely used by ophthalmologists to assist in diagnosis of glaucoma and is cost-effective. Methods: The morphological features of the disc that is characteristic of glaucoma are clearly seen in the fundus images. However, manual inspection of the acquired fundus images may be prone to inter-observer variation. Therefore, a computer-aided detection (CAD) system is proposed to make an accurate, reliable and fast diagnosis of glaucoma based on the optic nerve features of fundus imaging. In this paper, we reviewed existing techniques to automatically diagnose glaucoma. Results: The use of CAD is very effective in the diagnosis of glaucoma and can assist the clinicians to alleviate their workload significantly. We have also discussed the advantages of employing state-of-art techniques, including deep learning (DL), when developing the automated system. The DL methods are effective in glaucoma diagnosis. Conclusions:Novel DL algorithms with big data availability are required to develop a reliable CAD system. Such techniques can be employed to diagnose other eye diseases accurately.en_US
dc.language.isoenen_US
dc.relation.ispartofComputer methods and programs in biomedicineen_US
dc.rights© 2018 Elsevier B.V. All rights reserved.en_US
dc.subjectScience::Medicineen_US
dc.titleComputer-aided diagnosis of glaucoma using fundus images : a reviewen_US
dc.typeJournal Articleen
dc.contributor.schoolLee Kong Chian School of Medicine (LKCMedicine)en_US
dc.identifier.doi10.1016/j.cmpb.2018.07.012-
dc.identifier.pmid30337064-
dc.identifier.scopus2-s2.0-85051022554-
dc.identifier.volume165en_US
dc.identifier.spage1en_US
dc.identifier.epage12en_US
dc.subject.keywordsComputer-aided Detection Systemen_US
dc.subject.keywordsDeep Learningen_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
Appears in Collections:LKCMedicine Journal Articles

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