Developments in the detection of diabetic retinopathy: a state-of-the-art review of computer-aided diagnosis and machine learning methods
Author:
Funder
Ministry of Education, Malaysia
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Linguistics and Language,Language and Linguistics
Link
https://link.springer.com/content/pdf/10.1007/s10462-022-10185-6.pdf
Reference140 articles.
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3. Abràmoff MD, Lou Y, Erginay A, Clarida W, Amelon R, Folk JC, Niemeijer M (2016) Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol vis Sci 57(13):5200–5206
4. Aiello LP, Avery RL, Arrigg PG, Keyt BA, Jampel HD, Shah ST, Pasquale LR, Thieme H, Iwamoto MA, Park JE, Nguyen HV, Aiello LM, Ferrara N, King GL (1994) Vascular endothelial growth factor in ocular fluid of patients with diabetic retinopathy and other retinal disorders. N Engl J Med 331(22):1480–1487
5. Akyol K, Bayir Ş, Şen B (2016) Detection of hard exudates in diabetic retinopathy retinal images by utilizing visual dictionary and classifier approaches. Mugla J Sci Technol 2(1):1–6
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