Diagnosis and Grading of Diabetic Retinopathy using Deep Learning (International Journal of Food and Nutritional Sciences, 2022)
Diabetic retinopathy (DR), which causes tissue on the eye that damages visibility, is acommon complication of type-2 diabetes. If it is not discovered in time, total blindnessmight occur. DR is irreversible. DR is primarily among adults who are of working age. Morethan 150 million people are affected by diabetic retinopathy (DR), which accounts for 2.6%of blindness worldwide. Different indications of DR are vision distortion, bulging of the eye,and formation of irregular blood vessels. The traditional way is to use Computer-aidedDiagnosis (CAD) systems during treatment. The dataset used is the APTOS blindnessdetection dataset that is accessible in Kaggle. The Convolutional Neural Networks (CNN) isthe most effective way for classifying images. In this paper, the MobileNet architecture, adeep learning technique is utilized to automate the diagnosis of the disease and estimatethe severity of the eye into several stages through which the accuracy obtained for trainingis 95% and validation is 82%.