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Research Paper | Diabetology | India | Volume 14 Issue 1, January 2025 | Rating: 5.3 / 10
Diabetic Retinopathy Using Machine Learning
S. Shambavi, P. Kamaleshwari, R. Charumathi
Abstract: Diabetic retinopathy (DR), the most common diabetes complication and the leading cause of blindness in working - age adults, affects 27 - 35% of diabetics worldwide. Diabetic retinopathy (DR) is the most common diabetes condition and the leading cause of visual loss in working - age people [1]. Manually analyzing digital color fundus images for DR detection takes time and expertise. However, delayed results may cause patients to miss follow - up consultations and receive incorrect information [2]. The impact of advanced retinal glycation on retinal cell function and other diseases is examined. Early diabetes can progress to proliferative diabetic retinopathy (PDR) and diabetic macular edema (DME), so identifying important pathways could help design effective treatments [3]. Diabetes affects all retinal cells, but microvascular disease dominates retinal research. Neovascularization classifies diabetic retinopathy as proliferative (PDR) or non - proliferative. Although diabetes affects all retinal cells, most retinal research has concentrated on microvascular illness. Neovascularization divides diabetic retinopathy (DR) into non - proliferative (NPDR) and proliferative (PR) kinds. DRD from mellitus. Due to pancreatic adrenaline deficiency, diabetes causes chronic high blood glucose. DR increases the risk of vision degeneration in diabetics, especially low - income retirees. Preventing chronic diseases like diabetes requires early detection.
Keywords: diabetic retinopathy, machine learning, automated detection, fundus images, medical imaging
Edition: Volume 14 Issue 1, January 2025,
Pages: 471 - 474