IDENTIFYING DIABETIC RETINOPATHY USING CONVOLUTIONAL NEURAL NETWORK

Abstract

Diabetic retinopathy (DR) diagnosis by color fundus images needs trained practitioners to recognise the existence and significance of many minor abnormalities, which, combined with a complex grading system, makes this a challenging and time-consuming procedure.In this study, we present a CNN approach to detecting DR from digital fundus images and properly grading its severity.We create a network with CNN, Densnet 121 architecture, and data augmentation that can identify the intricate elements involved in the classification task, such as micro-aneurysms, exudate, and hemorrhages on the retina, and then deliver a diagnosis automatically and without user input.We train this network on the publically accessible Kaggle dataset with a high-end graphics processor unit (GPU) and achieve outstanding results, particularly for a high-level classification task.Our proposed CNN achieves a sensitivity of 95% on the data set of 80,000 photos used.