Automated detection of mild and multi-class diabetic eye diseases using deep learning

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Sarki, Rubina ORCID: 0000-0001-5018-9567, Ahmed, Khandakar ORCID: 0000-0003-1043-2029, Wang, Hua ORCID: 0000-0002-8465-0996 and Zhang, Yanchun ORCID: 0000-0002-5094-5980 (2020) Automated detection of mild and multi-class diabetic eye diseases using deep learning. Health Information Science and Systems, 8 (1). ISSN 2047-2501

Abstract

Diabetic eye disease is a collection of ocular problems that affect patients with diabetes. Thus, timely screening enhances the chances of timely treatment and prevents permanent vision impairment. Retinal fundus images are a useful resource to diagnose retinal complications for ophthalmologists. However, manual detection can be laborious and time-consuming. Therefore, developing an automated diagnose system reduces the time and workload for ophthalmologists. Recently, the image classification using Deep Learning (DL) in between healthy or diseased retinal fundus image classification already

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Item type	Article
URI	https://vuir.vu.edu.au/id/eprint/42852
DOI	10.1007/s13755-020-00125-5
Official URL	https://link.springer.com/article/10.1007%2Fs13755...
Subjects	Current > FOR (2020) Classification > 4602 Artificial intelligence Current > Division/Research > College of Science and Engineering
Keywords	diabetes, artificial intelligence, disease, detection, World Health Organisation, WHO, glaucoma, edema, cataract, retinopathy, eye health
Citations in Scopus	68 - View on Scopus
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