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- W4226133028 abstract "Diabetic retinopathy (DR) is a severe complication of eyes found in the diabetic patients. It is the main cause of loss of vision among them, and it progresses with the duration of the disease. Commonly, the diagnosis will be done by an ophthalmologist. But, the huge number of patients especially in the rural areas has a limited number of ophthalmologists that need to screen and review the images to properly diagnose the disease. If the number of out-patients are high, the doctor will be inclined to spend less amount of time for each patient. Automatic detection of diabetic retinopathy from the astronomically immense scale of retinal images avails the ophthalmologist to treat the affected patient and reduce chances of vision loss, developing an automated diagnosis software based on latest deep learning techniques. We have developed a framework for classification of features in the fundus images for the data sets IDRiD, Messidor, Diaret_db0 based on disease criticality. The GUI takes the retina images data set as the input. It pre-processes the images and does the features extraction. The extracted retinal features are helpful for the accurate diagnosis of DR. Based on these features, we can classify the criticality level of the disease of each image." @default.
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- W4226133028 date "2022-01-01" @default.
- W4226133028 modified "2023-09-27" @default.
- W4226133028 title "Framework for Diabetic Retinopathy Classification" @default.
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- W4226133028 doi "https://doi.org/10.1007/978-981-16-9669-5_4" @default.
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