Research Article Open Access

Approach of Deep Learning to detect Diabetes using Retinal Images

Abstract

Diabetes can cause diabetic retinopathy, which effect the retinal blood vessels and affects the eyes. It does not initially manifest symptoms or causes sporadic visual issues. When it becomes bad enough, it affects both eyes and eventually impairs vision completely or partially. Mostly happens when the blood sugar level is out of control. As a result, the chance of contracting this illness is always high for someone with diabetes mellitus. The risk of total and permanent blindness can be avoided with early diagnosis. Consequently, a reliable screening mechanism is needed. The densely connected convolutional network dense net is used in the current work to examine a deep learning approach for the early diagnosis of diabetic retinopathy. According to the severity levels, the fundus pictures are categorized as No DR, mild, moderate, severe, and proliferative DR. Diabetic retinopathy detection 2016 and aptos 2020 blindness detection, both collected from Kaggle, are the datasets that are considered. The phases included in the proposed technique are data collection, preprocessing, augmentation, and modelling. 92% accuracy was reached by our suggested model. The regression model, which was also used, had a 75% accuracy rate. The primary goal of this effort is to create a reliable method for automatically detecting DR.s.

Muhammad Muzammil

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