Combined Multi-Atlas and Multi-Layer Perception for Alzheimer's Disease Classification

Xin Hong; Kaifeng Huang; Jie Lin; Xiaoyan Ye; Guoxiang Wu; Longfei Chen; E Chen; Siyu Zhao

doi:10.3389/fnagi.2022.891433

Combined Multi-Atlas and Multi-Layer Perception for Alzheimer's Disease Classification

Front Aging Neurosci. 2022 Jun 1:14:891433. doi: 10.3389/fnagi.2022.891433. eCollection 2022.

Authors

Xin Hong^{1

2}, Kaifeng Huang¹, Jie Lin¹, Xiaoyan Ye³, Guoxiang Wu⁴, Longfei Chen⁵, E Chen⁶, Siyu Zhao¹

Affiliations

¹ College of Computer Science and Technology, Huaqiao University, Xiamen, China.
² Key Laboratory of Computer Vision and Machine Learning (Huaqiao University), Fujian Province University, Xiamen, China.
³ Fuzhou Comvee Network and Technology Co., Ltd, Fuzhou, China.
⁴ College of Foreign Languages, Huaqiao University, Quanzhou, China.
⁵ Department of Neurology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
⁶ Department of Neurosurgery, Zhongshan Hospital Affiliated to Xiamen University, Xiamen, China.

Abstract

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. To distinguish the stage of the disease, AD classification technology challenge has been proposed in Pattern Recognition and Computer Vision 2021 (PRCV 2021) which provides the gray volume and average cortical thickness data extracted in multiple atlases from magnetic resonance imaging (MRI). Traditional methods either train with convolutional neural network (CNN) by MRI data to adapt the spatial features of images or train with recurrent neural network (RNN) by temporal features to predict the next stage. However, the morphological features from the challenge have been extracted into discrete values. We present a multi-atlases multi-layer perceptron (MAMLP) approach to deal with the relationship between morphological features and the stage of the disease. The model consists of multiple multi-layer perceptron (MLP) modules, and morphological features extracted from different atlases will be classified by different MLP modules. The final vote of all classification results obtains the predicted disease stage. Firstly, to preserve the diversity of brain features, the most representative atlases are chosen from groups of similar atlases, and one atlas is selected in each group. Secondly, each atlas is fed into one MLP to fetch the score of the classification. Thirdly, to obtain more stable results, scores from different atlases are combined to vote the result of the classification. Based on this approach, we rank 10th among 373 teams in the challenge. The results of the experiment indicate as follows: (1) Group selection of atlas reduces the number of features required without reducing the accuracy of the model; (2) The MLP architecture achieves better performance than CNN and RNN networks in morphological features; and (3) Compared with other networks, the combination of multiple MLP networks has faster convergence of about 40% and makes the classification more stable.

Keywords: Alzheimer's disease; PRCV competition; atlas; classification; multi-layer perceptron.