Abstract
Due to growing trend of urban living, there is a considerable increase in the consumption of processed carbohydrates and sugar by the people, it is found to be one of the very common reasons for diabetes being on the rise worldwide. Diabetic Retinopathy is a disease prone to diabetic people which causes severe damage to the retina of the eye, it is observed that most of the patients with diabetes also suffer from partial or complete loss of their vision if not detected in their early stages. The aim of this paper is to achieve autonomy in the diagnosis of Diabetic Retinopathy (DR), that is achieved by categorizing different DR affected fundus images into 5 sections (0-Normal, 1-Mild, 2-Moderate, 3-Severe, 4-Proliferative) and then training Dense Convolutional Network (DenseNet) algorithm which is one of the efficient Convolution Neural Network (CNN) using these 5 sections, by which accurate identification and diagnosis of the input image is achieved. This autonomous process will help the physician in treating DR easily, saving physicians time and reducing diagnostic errors.
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Viraktamath, S.V., Hiremath, D., Tallur, K. (2023). Detection of Diabetic Retinopathy Using Fundus Images. In: Shetty, N.R., Patnaik, L.M., Prasad, N.H. (eds) Emerging Research in Computing, Information, Communication and Applications. Lecture Notes in Electrical Engineering, vol 928. Springer, Singapore. https://doi.org/10.1007/978-981-19-5482-5_30
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DOI: https://doi.org/10.1007/978-981-19-5482-5_30
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