Detection of Diabetic Retinopathy Using Bichannel Convolutional Neural Network
Shu‐I Pao, Hong-Zin Lin, Ke‐Hung Chien, Ming‐Cheng Tai, Jiann‐Torng Chen, Gen‐Min Lin
Abstract
Deep learning of fundus photograph has emerged as a practical and cost-effective technique for automatic screening and diagnosis of severer diabetic retinopathy (DR). The entropy image of luminance of fundus photograph has been demonstrated to increase the detection performance for referable DR using a convolutional neural network- (CNN-) based system. In this paper, the entropy image computed by using the green component of fundus photograph is proposed. In addition, image enhancement by unsharp masking (UM) is utilized for preprocessing before calculating the entropy images. The bichannel CNN incorporating the features of both the entropy images of the gray level and the green component preprocessed by UM is also proposed to improve the detection performance of referable DR by deep learning.