Deep Convolutional Neural Networks for Diabetic Retinopathy Classification

Abstract

Diabetic retinopathy (DR) is one of the leading cause of blindness, but the classification of DR requires experienced ophthalmologist to distinguish the presence of various small features, which is time-consuming and difficult. Convolution neural network (CNN), which enables learning hierarchical and discriminative features without experiences of clinicians, is an alternative method to address the above issue. In this paper, we investigate four factors of employing deep CNN to DR classification problem, including network architectures, preprocessing, class imbalance and fine-tuning. The best performance we achieved is an accuracy of 79% opposed to an accuracy of 75% for Harry's scheme [14].