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Diabetic retinopathy (DR) is an ocular manifestation of diabetes and the leading cause of visual impairment and blindness across the globe. Early detection and treatment of DR can salvage from visual impairment. The manual screening of DR is a very laborious and time-intensive effort and heavily dependent on professional ophthalmologists. In addition, the subtle distinction among various retinal biomarkers and different grades of DR makes this recognition very challenging. To address the aforementioned problem, deep neural networks have brought many revolutions in the last few years. In this study, we proposed a novel two-stage framework for automatic DR classification. In the first stage, we employed two distinct U-Net models for optic disc (OD) and blood vessel (BV) segmentation during the preprocessing. In the second stage, the enhanced retinal images after OD and BV extraction are used as an input of transfer learning-based model VGGNet, which performs DR detection by identifying retinal biomarkers such as microaneurysms (MA), haemorrhages (HM), and exudates (EX). The proposed model achieved state-of-the-art performance with an average accuracy of 96.60%, 93.95%, 92.25% evaluated on EyePACS-1, Messidor-2, and DIARETDB0, respectively. Extensive experiments and comparison with baseline methods demonstrate that the effectiveness of the proposed approach.

A Transfer Learning and U-Net-based automatic detection of diabetic retinopathy from fundus images