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An fMRI Sequence Representation Learning Framework for Attention Deficit Hyperactivity Disorder Classification

Authors :
Jin Xie
Zhiyong Huo
Xianru Liu
Zhishun Wang
Source :
Applied Sciences, Vol 12, Iss 12, p 6211 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

For attention deficit hyperactivity disorder (ADHD), a common neurological disease, accurate identification is the basis for treatment. In this paper, a novel end-to-end representation learning framework for ADHD classification of functional magnetic resonance imaging (fMRI) sequences is proposed. With such a framework, the complexity of the sequence representation learning neural network decreases, the overfitting problem of deep learning for small samples cases is solved effectively, and superior classification performance is achieved. Specifically, a data conversion module was designed to convert a two-dimensional sequence into a three-dimensional image, which expands the modeling area and greatly reduces the computational complexity. The transfer learning method was utilized to freeze or fine-tune the parameters of the pre-trained neural network to reduce the risk of overfitting in the cases with small samples. Hierarchical feature extraction can be performed automatically by combining the sequence representation learning modules with a weighted cross-entropy loss. Experiments were conducted both with individual imaging sites and combining them, and the results showed that the classification average accuracies with the proposed framework were 73.73% and 72.02%, respectively, which are much higher than those of the existing methods.

Details

Language :
English
ISSN :
20763417
Volume :
12
Issue :
12
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.71be934fbcc94b259ce09a28b5075dc1
Document Type :
article
Full Text :
https://doi.org/10.3390/app12126211