Deep Learning Through Two-Branch Convolutional Neuron Network for Glaucoma Diagnosis

Yidong Chai, Luo He, Qiuyan Mei, Hongyan Liu, Liang Xu

Abstract: Glaucoma is a group of eye diseases that damage the optic nerves progressively and lead to deterioration in vision irreversibly. Diagnosing glaucoma based on retinal images automatically is meaningful both in practice and research area. While deep learning models have achieved superior performance in natural images recognition and have been also used for medical image diagnosis recently, the models usually rely on large dataset and expensive computing resources, thus limiting the wider use in medical areas. So how to train a deep learning model with relatively small amount of medical data is challenging. In this paper, we propose to incorporate domain knowledge to construct a two-branch Convolutional Neural Networks (CNN) to learn a classifier for glaucoma diagnosis based on the retinal image. Our two-branch CNN framework can analyze the whole image and pay special attention to discriminative local region of image at the same time. Experiments conducted on real medical dataset demonstrate the advantages of our method over traditional computer vision algorithm and classical CNN.