摘要
由于对眼底视网膜图像进行人工诊断分类时存在效率低、漏诊、误诊等情况,为辅助医师辨别多种疾病的眼底筛查结果,降低漏检及误检率,提出一种基于注意力机制SENet的卷积网络模型和GBDT梯度提升的分类方法来解决视网膜疾病分类问题.该模型在深度学习模型的基础上,利用采样卷积网络对提取的视网膜出血、视盘水肿、黄斑区病变这3种特征进行学习,通过GBDT梯度提升的方法进行识别和分类,并采用大连市第三人民医院提供的真实临床数据对所提方法的性能进行评价.结果表明,该模型在平均准确率,精确率和召回率分别达到99.27%,98.35%,0.9810,在视网膜疾病临床诊断中具有一定的实用价值.
Low efficiency,missed diagnosis and misdiagnosis exist in the manual diagnosis and classification of fundus retinal images.To this end,a convolutional network model based on the attention mechanism SENet and GBDT gradient boosting classification method is proposed to help physicians distinguish the fundus screening results of various diseases and reduce the rate of missed and false detection.Based on the deep learning model,the sampling convolutional network is applied to learn the extracted three characteristics of retinal hemorrhage,optic disc edema and macular degeneration,and the GBDT gradient boosting method is employed for identification and classification.The real clinical data provided by the Third People’s Hospital of Dalian are used to evaluate the performance of the proposed method.The results show that the average accuracy,precision,and recall rates of the model reach 99.27%,98.35%,and 0.9810 respectively,and the model has certain practical value in the clinical diagnosis of retinal diseases.
作者
陈可心
乔焕
方玲玲
CHEN Ke-Xin;QIAO Huan;FANG Ling-Ling(School of Computer Science and Artificial Intelligence,Liaoning Normal University,Dalian 116081,China)
出处
《计算机系统应用》
2023年第8期286-294,共9页
Computer Systems & Applications
基金
辽宁省自然科学基金(2021-MS-272)
辽宁省教育厅项目(LJKQZ2021088)。
关键词
眼底视网膜图像
卷积神经网络
注意力机制
图像分类
fundus retinal image
convolution neural network(CNN)
attention mechanism
image classification
作者简介
通信作者:方玲玲,E-mail:fanglingling@lnnu.edu.cn。
