Classification model of Alzheimer's disease based on deep learning and multimodal physiological data
10.19745/j.1003-8868.2023220
- VernacularTitle:基于深度学习与多模态生理数据的阿尔茨海默病分类方法研究
- Author:
Jing-Xuan WANG
1
;
Wen-Jing WANG
;
Liang WEN
;
Zhen-Ni LI
Author Information
1. 东北大学信息科学与工程学院,沈阳 110819
- Keywords:
deep learning;
multimodal physiological data;
Alzheimer's disease;
New_ResNet 50 network;
3D-Unet-Attention network;
multi-layer perception network
- From:
Chinese Medical Equipment Journal
2023;44(11):1-8
- CountryChina
- Language:Chinese
-
Abstract:
Objective To propose an Alzheimer's disease(AD)classification method based on deep learning and multimodal physiological data.Methods Multimodal data from the Alzheimer's Disease Neuroimaging Initiative(ADNI)database of AD patients,early mild cognitive impairment(EMCI)patients,late mild cognitive impairment(LMCI)patients and normal cognition(NC)subjects were selected.Three networks were used for AD classification,of which an improved New_ResNet50 network extracted the features of MRI images of the subject's brain to realize AD classification,a 3D-Unet-Attention network segmented the hippocampus images and implemented residual network-based AD classification,and a multi-layer perception(MLP)network carried out AD classification based on patient physiological data and hippocampus size,and the final classification results were determined with the voting method.Comparison analyses were performed on the classification results by the improved New_ResNet50 network model,3D-Unet-Attention network model or traditional network models,and the improved New_ResNet50 network model,3D-Unet-Attention network model and MLP network model were all compared with the fusion network model involving in the three networks model above.Results The improved New_ResNet50 network model and 3D-Unet-Attention network model both had the classification accuracy enhanced when compared with the traditional network models,and the fusion network model had a classification accuracy of 97.99％for AD patients and control normal,which was higher by 1.51％,1.51％and 14.62％than those by the improved New_ResNet50 network model,3D-Unet-Attention network model and MLP network model respectively.Conclusion The classification method proposed behaves well for AD classification,and can be used for auxiliary diagnosis of AD.[Chinese Medical Equipment Journal,2023,44(11):1-8]
