Abstract
Early detection of Diabetic Retinopathy shields patients from losing their vision because Diabetic Retinopathy may be a typical eye disorder in diabetic patients. The elemental explanation for a visual deficiency within the populace. Thus, this paper proposes an automated method for image-based classification of diabetic retinopathy. The technique is separated into three phases: image processing, feature extraction, and image classification. The target is to naturally group the evaluation of non-proliferative diabetic retinopathy at any retinal image. For that, an underlying image preparing stage separates blood vessels, microaneurysms, and hard exudates, so on extricate highlights utilized by a calculation to make sense of the retinopathy grade.
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Bandgar, V.V., Bewoor, S., Fattepurkar, G.A., Chaudhary, P.B. (2021). Early Detection of Diabetic Retinopathy Using Machine Learning. In: Pawar, P.M., Balasubramaniam, R., Ronge, B.P., Salunkhe, S.B., Vibhute, A.S., Melinamath, B. (eds) Techno-Societal 2020. Springer, Cham. https://doi.org/10.1007/978-3-030-69921-5_23
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DOI: https://doi.org/10.1007/978-3-030-69921-5_23
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