ABSTRACT
Diabetic retinopathy (DR) is a constantly deteriorating disease, being one of the leading causes of vision impairment and blindness. Subtle distinction among different grades and existence of many significant small features make the task of recognition very challenging. Early-stage detection of DR is very important for diagnosis, which can prevent blindness with proper treatment. In this chapter, we have developed a novel deep convolutional neural network), which performs the early-stage detection by identifying all microaneurysms, the first signs of DR, along with correctly assigning labels to retinal fundus images into five categories. We have tested our network on the largest publicly available Kaggle DR dataset and have achieved a state-of-the-art performance of 0:851 quadratic weighted kappa score and 0:844 AUC AU: Please define “AUC”. score on severity grading. In the early-stage detection, we have achieved a sensitivity of 98% and specificity of above 94%, which demonstrates the effectiveness of our proposed method. Our proposed architecture is very simple and efficient with respect to computational time and space at the same time.
