Deep Learning Based Approach for Fully Automated Detection and Segmentation of Hard Exudate from Retinal Images

Diabetic retinopathy (DR), which is a major cause of blindness in the world is characterized by hard exudate lesions in the eyes as these lesions are one of the most prevalent and earliest symptoms of DR. In this paper, a fully automated method for hard exudate delineation is described that could assist ophthalmologists for timely diagnosis of DR before disease progress to a level beyond treatment. We used a dataset consist of 107 images to develop a U-Net-based method for hard exudate detection and segmentation. This network consists of shrinking and expansive streams in which shrinking path has the same structure as conventional convolutional networks. In expansive path, obtained features are merged with those from shrinking path with the proper resolution to generate multi-scale features and accomplish distinction between hard exudate and normal tissue in retinal images. The training images were augmented artificially to increase the number of samples in the dataset and avoid overfitting issues. Experimental results showed that our proposed method reported sensitivity, specificity, accuracy, and Dice similarity coefficient of 96.15%, 80.77%, 88.46%, and 67.23 +/- 13.60% on 52 test images, respectively.