A Novel Deep Learning Method for Red Lesions Detection Using Hybrid Feature

Red lesions detection is the key to controlling disease progression in Diabetic Retinopathy (DR) early stages. In this paper we propose a novel method for red lesions detection based on hybrid features, which consist of deep learned features extracted via an improved LeNet architecture and hand-crafted features. A class balanced cross-entropy loss in full connected layer of the modified LeNet network is used to reduce the interference from the unbalanced data types on learning features. Blood vessels segmentation based on the U-net Convolutional Network is applied to deal with the lesion candidates overlapping with vessels in the process of hand-crafted features extraction. Such ensemble vector of descriptors is used afterwards to identify true lesion candidates using a Random Forest classifier. The approach was evaluated based on the public dataset-DIARETDB1.