  Diabetic retinopathy refers to the pathology of the retina induced by
diabetes and is one of the leading causes of preventable blindness in the
world. Early detection of diabetic retinopathy is critical to avoid vision
problem through continuous screening and treatment. In traditional clinical
practice, the involved lesions are manually detected using photographs of the
fundus. However, this task is cumbersome and time-consuming and requires
intense effort due to the small size of lesion and low contrast of the images.
Thus, computer-assisted diagnosis of diabetic retinopathy based on the
detection of red lesions is actively being explored recently. In this paper, we
present a convolutional neural network with residual skip connection for the
segmentation of exudates in retinal images. To improve the performance of
network architecture, a suitable image augmentation technique is used. The
proposed network can robustly segment exudates with high accuracy, which makes
it suitable for diabetic retinopathy screening. Comparative performance
analysis of three benchmark databases: HEI-MED, E-ophtha, and DiaretDB1 is
presented. 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, and
DiaReTDB1, respectively.
