Deep Learning Techniques for Predictive Modeling of Diabetic Eye Disease in Type 2 Diabetes: A Systematic Review

Diabetic retinopathy (DR) is a common complication of type 2 diabetes. It occurs when high blood sugar levels damage the blood vessels in the retina, the light-sensitive tissue at the back of the eye. While diabetic retinopathy can occur in both type 1 and type 2 diabetes, it is indeed more commonly associated with type 2 diabetes due to its higher prevalence and longer duration in many cases. Type 2 diabetes often develops gradually over time, allowing for prolonged exposure to elevated blood sugar levels. This prolonged exposure increases the risk of developing diabetic retinopathy and other diabetes-related complications. The aim of this paper is to analyze the various deep learning models for effective prediction of diabetic retinopathy in patients suffering from Type 2 Diabetes. Furthermore, standard datasets consisting of 38,788 training and 55,504 test images for diabetic retinopathy and blindness are obtained. On the other hand, deep learning models such as ResNet101V2, DenseNet201, InceptionResNetV2, EfficientNetB7, and Xception CNNs are applied to the dataset and trained as well. Moreover, the performance of all the models is assessed on the basis of certain quality measures, such as accuracy, F1 score, recall, precision, RMSE values, and loss. On the other hand, results indicate the potential of deep learning models in accurately predicting diabetic retinopathy, thereby aiding in early diagnosis and intervention to prevent vision loss in patients with Type 2 Diabetes.