Automatic Diagnosis of Familial Exudative Vitreoretinopathy Using a Fusion Neural Network for Wide-Angle Retinal Images

Abstract

Familial exudative vitreoretinopathy (FEVR) is a hereditary disorder that can damage the retina. This retinal damage can lead to vision loss and even blindness in the late stages. Thus, early diagnosis and prevention of the disease's progression are critical. The purpose of this study was to develop an automated diagnosis system for FEVR based on combining deep learning and domain knowledge. A transfer learning scheme was designed to train a deep convolutional neural network (DCNN) to provide segmentation of the retinal vessels. Based on this vessel segmentation and prior clinical knowledge, the vascular characteristics, including the retinal avascular area, vessel angle, fractal dimension, branching and density of blood vessels, were automatically evaluated. Finally, the diagnosis of FEVR was achieved by a feature fusion neural network. Our method was evaluated on 300 images with 168 healthy and 132 FEVR images. By combining deep features and handcrafted features (extracted vascular characteristics), the proposed method achieved an average F1-score of 0.95, with excellent accuracy (94.34%) and sensitivity (91.43%); the quadratic weighted κ was 0.88 for the diagnosis of FEVR. We demonstrated the effectiveness and robustness of the proposed method using five-fold cross-validation. The proposed automatic diagnosis system can assist doctors for better judgment and make sense of early diagnosis and prevention of the disease's progression.