Machine Learning Based Diabetic Retinopathy Detection and Classification
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-34873-0_5
Reference124 articles.
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3. Mookiah MRK, Acharya UR, Chua CK, Lim CM, Ng EYK, Laude A (2013) Computer-aided diagnosis of diabetic retinopathy: a review. Comput Biol Med 43:2136–2155
4. Peng JJ, Xiong SQ, Ding LX, Peng J, Xia XB (2019) Diabetic retinopathy: focus on NADPH oxidase and its potential as therapeutic target. Eur J Pharmacol 853:381–387
5. Wilkinson CP, Ferris FL, Klein RE, Lee PP, Agardh CD, Davis M et al (2003) Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology 110(9):1677–1682
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1. CGSX Ensemble: An Integrative Machine Learning and Deep Learning Approach for Improved Diabetic Retinopathy Classification;International Journal of Electrical and Electronics Research;2024-06-28
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