Hierarchical Feature Extraction for Early Alzheimer's Disease Diagnosis

Lulu Yue, Xiaoliang Gong, Jie Li, Hongfei Ji, Maozhen Li, Asoke K. Nandi
2019 IEEE Access  
Mild cognitive impairment (MCI) is the early stage of Alzheimer's disease (AD). In this paper, we propose a novel voxel-based hierarchical feature extraction (VHFE) method for the early AD diagnosis. First, we parcellate the whole brain into 90 regions of interests (ROIs) based on an automated anatomical labeling (AAL) template. To split the uninformative data, we select the informative voxels in each ROI with a baseline of their values and arrange them into a vector. Then, the first stage
more » ... res are selected based on the correlation of the voxels between different groups. Next, the brain feature maps of each subject made up of the fetched voxels are fed into a convolutional neural network (CNN) to learn the deeply hidden features. Finally, to validate the effectiveness of the proposed method, we test it with the subset of the AD neuroimaging (ADNI) database. The testing results demonstrate that the proposed method is robust with a promising performance in comparison with the state-of-the-art methods. INDEX TERMS Alzheimer's disease, convolutional neural network, hierarchical feature extraction, mild cognitive impairment. 93752 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/ VOLUME 7, 2019
doi:10.1109/access.2019.2926288 fatcat:6avozb3msjhtxpsprhn3ov7zay