Deep Learning-based Diabetic Retinopathy Detection for automated health assessment

Diabetic Retinopathy(DR) is a type of chronic microvascular diabetes complication that deteriorates the human vision system and leads to a patient towards complete blindness. DR is a common condition among people with diabetes, affecting an estimated 93 million people. Early detection of DR is a vital strategy to alleviate massive vision impairment. In this research, we have experienced DR patients and tried to determine whether they are DR affected or not. We present a lightweight and quick detection system for classifying DR over fundus images of two sets: healthy retina or DR-affected retina. After pre-processing, we produced the dataset annotations needed for model training. Next, we add the MobilenetV2 to Depthwise Separable Convolution (DSC) level to compute the representative collection of data. The proposed system is trained in a standard approach for further classification using a cross-entropy loss function. We have performed comprehensive testing on a dataset of 3662 fundus images from the Kaggle competition. The proposed model performs rapid detection with a high-level accuracy of 97.82%. It also obtained 95% sensitivity and 96% specificity on the test dataset. We have evaluated our method with the existing contemporary approaches to highlight its robustness based on DR classification. We have achieved incredible results over other traditional methods based on computational complexity and training time. The proposed early DR detection system will be a promising tool for improving the management and treatment of diabetic patients.