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

Full text for this resource is not available from the Research Repository.

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


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

Dimensions Badge

Altmetric Badge

Item type Article
DOI 10.1007/s13755-020-00125-5
Official URL
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
Download/View statistics View download statistics for this item

Search Google Scholar

Repository staff login