Automated Detection of Diabetic Retinopathy using a Binocular Siamese-Like Convolutional Network

Abstract

Diabetic retinopathy (DR) is an important causes of blindness worldwide. It is hard to detected in early stages and the diagnostic procedure can be time-consuming. Therefore, we proposed a deep learning method to automatedly diagnose the referable deiabetic retinopathy (RDR) by classifying retinal fundus photographs. In our work, A novel convolutional neural network with Siamese-like architecture is trained with transfer learning technique. Different from previous works, the proposed model accepts binocular fundus images as input and learns their correlation to aid the prediction. A custom loss function combining cross entropy loss and contrastive loss is also adopted to guide the gradient descent. With a training set of only 28104 images and a test set of 3510 images, an area under the receiver operating curve (AUC) of 0.949 is obtained through the proposed method. Furthermore, the sensitivity reaches 80.7% when the specificity is fixed at 95.0%, and the specificity reaches 70.3% when the sensitivity is fixed at 95.0%, which are 3.2% and 4.6% higher than those obtained by the existing Inception V3 model trained with monocular fundus images, respectively.