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Diabetic retinopathy refers to the pathology of the retina induced bydiabetes and is one of the leading causes of preventable blindness in theworld. Early detection of diabetic retinopathy is critical to avoid visionproblem through continuous screening and treatment. In traditional clinicalpractice, the involved lesions are manually detected using photographs of thefundus. However, this task is cumbersome and time-consuming and requiresintense effort due to the small size of lesion and low contrast of the images.Thus, computer-assisted diagnosis of diabetic retinopathy based on thedetection of red lesions is actively being explored recently. In this paper, wepresent a convolutional neural network with residual skip connection for thesegmentation of exudates in retinal images. To improve the performance ofnetwork architecture, a suitable image augmentation technique is used. Theproposed network can robustly segment exudates with high accuracy, which makesit suitable for diabetic retinopathy screening. Comparative performanceanalysis of three benchmark databases: HEI-MED, E-ophtha, and DiaretDB1 ispresented. It is shown that the proposed method achieves accuracy (0.98, 0.99,0.98) and sensitivity (0.97, 0.92, and 0.95) on E-ophtha, HEI-MED, andDiaReTDB1, respectively.

A Residual Encoder-Decoder Network for Segmentation of Retinal Image-Based Exudates in Diabetic Retinopathy Screening