Hierarchical Feature Extraction for Early Alzheimer's Disease Diagnosis

• Published in: IEEE access Vol. 7; pp. 93752 - 93760
• Main Authors: Yue, Lulu, Gong, Xiaoliang, Li, Jie, Ji, Hongfei, Li, Maozhen, Nandi, Asoke K.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Alzheimer's disease; Artificial neural networks; Brain; convolutional neural network; Convolutional neural networks; Correlation; Diagnosis; Feature extraction; Feature maps; hierarchical feature extraction; Magnetic resonance imaging; Medical imaging; mild cognitive impairment
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2926288

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 features 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.