文献类型：期刊文献

中文题名：基于R-FCN算法的糖尿病眼底病变自动诊断

英文题名：Automatic Detection of Diabetic Retinopathy Based on R-FCN Algorithm

作者：王嘉良[1];罗健旭[1];刘斌[2];冯瑞[3];邹海东[4]

机构：[1]华东理工大学信息科学与工程学院,上海200237;[2]上海无线电设备研究所,上海200000;[3]复旦大学计算机科学与工程学院,上海200433;[4]上海市眼科医院,上海200040

年份：2020

卷号：56

期号：4

起止页码：109

中文期刊名：计算机工程与应用

外文期刊名：Computer Engineering and Applications

收录：CSTPCD;;北大核心:【北大核心2017】；CSCD:【CSCD_E2019_2020】；

基金：上海市科技创新行动计划（No.17511107900）

语种：中文

中文关键词：深度学习;目标检测;卷积神经网络;计算机视觉;糖尿病眼底病变

外文关键词：deep learning;object detection;convolution neural networks;computer vision;diabetic retinopathy

摘要：糖尿病眼底病变(Diabetic Retinopathy,DR)是糖尿病患者常见的致盲疾病,可使用深度学习算法对患者的糖尿病眼底图片进行图像识别,实现对糖尿病眼底病变的辅助诊断。针对以往普通卷积神经网络只能进行分类和输入尺寸固定的问题,提出了基于目标检测的区域全卷积网络(Region-based Fully Convolutional Networks,R-FCN)算法,实现同时对任意尺寸输入的糖尿病眼底图片的分类和病变区域检测。针对原始R-FCN算法对小目标(极小的出血点和血管瘤)检测困难的问题,对R-FCN算法做了一定的改进,加入特征金字塔网络(Feature Pyramid Networks,FPN)结构,升级主干网络,修改区域建议网络(Region Proposal Network,RPN)。实现结果表明,改进后的RFCN算法能以很高的正确率实现对糖尿病眼底图片的五级分类(健康、轻度、中度、重度、增殖)和病变区域检测(血管瘤、眼底出血、玻璃体出血)。
Diabetic Retinopathy(DR)is a leading cause of blindness.In order to diagnose diabetic retinopathy efficiently,deep learning algorithm is applied to detect diabetic retinopathy images.As the plain convolution neural networks can only classify images and the scale of input must be fixed,Region-based Fully Convolutional Networks(R-FCN)object detection algorithm is proposed to classify retinal fundus images and detect lesion regions with any scales.To solve the problem that the original R-FCN algorithm is difficult to detect small objects(minimal retinal hemorrhage and microaneurysm),the R-FCN algorithm is modified by adding the FPN(Feature Pyramid Networks),upgrading the backbone network,and modifying the RPN(Region Proposal Network).The results show that the modified R-FCN algorithm can achieve high accuracy in the classification of retinal fundus images(healthy,mild,moderate,severe and proliferative)and the detection of lesion regions(microaneurysm,retinal hemorrhage and vitreous hemorrhage).