摘要
糖尿病视网膜病变(Diabetic Retinopathy,DR)是糖尿病的一个常见的急性阶段,可引起视网膜的视功能异常。针对视网膜眼底图像病灶区域识别困难以及分级效率不高等问题,本文提出一种基于注意力机制多特征融合的算法来对DR进行诊断分级。首先对输入的图像采用高斯滤波等形态学预处理来提升眼底图像特征对比度;然后用ResNeSt50残差网络作为模型的骨干,引入多尺度特征增强模块对视网膜病变图像病变区域进行特征增强,提高分级准确率;再后利用图形特征融合模块对主干输出的特征增强后的局部特征进行信息融合;最后采用中心损失和焦点损失组合的加权损失函数进一步提升分类效果。在印度糖尿病视网膜病变(IDRID)数据集中灵敏度和特异性分别为95.65%和91.17%,二次加权一致性检验系数为90.38%。在Kaggle比赛数据集中准确率为84.41%,受试者工作特征曲线下的面积为90.36%。仿真实验表明,本文算法在糖尿病视网膜病变分级中具有一定的应用价值。
Diabetic Retinopathy(DR)is a prevalent acute stage of diabetes mellitus that causes vision-effecting abnormalities on the retina.In view of the difficulty in identifying the lesion area in retinal fundus images and the low grading efficiency,this paper proposes an algorithm based on multi-feature fusion of attention mechanism to diagnose and grade DR.Firstly,morphological preprocessing such as Gaussian filtering is applied to the input image to improve the feature contrast of the fundus image.Secondly,the ResNeSt50 residual network is used as the backbone of the model,and a multi-scale feature enhancement module is introduced to enhance the feature of the lesion area of the retinopathy image to improve the classification accuracy.Then,the graphic feature fusion module is used to fuse the enhanced local features of the main output.Finally,a weighted loss function combining center loss and focal loss is used to further improve the classification effect.In the Indian Diabetic Retinopathy(IDRID)dataset,the sensitivity and specificity were 95.65%and 91.17%,respectively,and the quadratic weighted agreement test coefficient was 90.38%.In the Kaggle competition dataset,the accuracy rate is 84.41%,and the area under the receiver operating characteristic curve was 90.36%.Simulation experiments show that the proposed algorithm has certain application value in the grading of diabetic retinopathy.
作者
梁礼明
董信
李仁杰
何安军
Liang Liming;Dong Xin;Li Renjie;He Anjun(School of Electrical Engineering and Automation,Jiangxi University of Science and Technology,Ganzhou,Jiangxi 341000,China)
出处
《光电工程》
CAS
CSCD
北大核心
2023年第1期98-109,共12页
Opto-Electronic Engineering
基金
国家自然科学基金资助项目(51365017,6146301)
江西省自然科学基金资助项目(20192BAB205084)。
