Heterogeneous modular deep neural network for diabetic retinopathy detection

Abstract

This paper proposes heterogeneous modular deep neural network (DNN) to address a complex problem of detection of diabetic retinopathy and simultaneously the five types of abnormalities. The modular approach gives the advantage to extract class specific features for the classifier, which helps to outperform the classical convolutional neural networks. Moreover, the heterogeneous nature of modular DNN provides the economy in the overall architecture and also enables to extract region specific features which further contribute to higher accuracy of detection. Extensive simulation studies were performed using benchmark dataset DIARETDB0 and results show that the proposed approach performs better or equivalently good than the other standard approaches.