基于多尺度残差神经网络的阿尔茨海默病诊断分类  

The diagnosis of Alzheimer's disease classification based on multi-scale residual neutral network

作　　者：刘振丙[1] 方旭升 杨辉华[1,2] 蓝如师 LIU Zhenbing;FANG Xusheng;YANG Huihua;LAN Rushi(School of Electronic Engineering and Automation,Guilin University of Electronic Technology, Guilin 541000,Guangxi,China;School of Automation,Beijing University of Electronic Technology,Beijing 100876,China)

机构地区：[1]桂林电子科技大学电子工程与自动化学院,广西桂林541000 [2]北京邮电大学自动化学院,北京100876

出　　处：《山东大学学报（工学版）》2018年第6期1-7,18,共8页Journal of Shandong University（Engineering Science）

基　　金：国家自然科学基金项目(61562013;61866009);广西自然科学基金(2017GXNFDA198025)

摘　　要：提出多尺度残差神经网络(multi-scale resnet,MSResnet)。采用不同大小的卷积核对图像进行多尺度信息采集,并对神经网络进行残差学习,避免网络退化。对核磁共振图像(magnetic resonance imaging,MRI)进行标准化处理,利用MSResnet模型在阿尔茨海默症(Alzheimer's disease,AD)和正常受试者(normal control,NC)获得的分类准确率为99. 41%,在AD和轻度认知障碍(mild cognitive impairment,MCI)获得分类准确率为97. 35%。与已有的算法相比,本研究提出的算法的分类准确率得到了明显的提高。A multi-scale resnet(MSResnet)method was proposed in this paper,which employed multi-scale convolution kernel to extract multi-scale information of structural magnetic resonance imaging MRI,and carried out residual learning for neural network, so as to avoid network degradation.After the gray scale standardization of MRI,the 99.41% classification precision was obtained by using the MSResnet model between Alzheimer's disease(AD)and normal control(NC),and the classification accuracy between AD and mild cognitive impairment(MCI)was 97.35%.Compared with the existing approaches,the algorithm proposed in this paper improved the classification accuracy significantly.

关 键 词：多尺度残差神经网络 核磁共振图像 阿尔茨海默病 灰度标准化 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]