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

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.