Detection of Diabetic Retinopathy (International Journal of Food and Nutritional Sciences, 2022)
Biomedical engineering faces a significant challenge in assessing the physiological changesoccurring in the human body non-invasively, particularly when detecting abnormalities inthe eye due to the complexities involved. Traditional disease identification techniques inretinal images rely on manual intervention, which can be error-prone and have a lowsuccess rate. Long-term diabetics are susceptible to the multistage progressive conditionknown as diabetic retinopathy, which has aberrant retinal imaging characteristics suchmicroaneurysms, haemorrhages, and exudates. It uses the dataset which consists of total13970 images which includes the images of all stages collected from kaggle APTOScompetition. Deep learning, a key method in health informatics, is used in this work toidentify diabetic retinopathy and its stages.. Residual Network and Densely connectedconvolutional network deep learning approaches are employed, and transfer learning isused to achieve good accuracy in identifying the stages of diabetic retinopathy. Theaccuracy obtained by using the ResNet101 is 92.37% and with the DenseNet121 is 98.37%.