A multi-view convolutional neural network method combining attention mechanism for diagnosing autism spectrum disorder

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition whose current psychiatric diagnostic process is subjective and behavior-based. In contrast, functional magnetic resonance imaging (fMRI) can objectively measure brain activity and is useful for identifying brain disorders. However, the...

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Bibliographic Details
Published in:PloS one 2023-12, Vol.18 (12), p.e0295621-e0295621
Main Authors: Wang, Mingzhi, Ma, Zhiqiang, Wang, Yongjie, Liu, Jing, Guo, Jifeng
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Analysis
Artificial neural networks
Attention
Autism
Brain
Brain mapping
Care and treatment
Data integration
Datasets
Diagnosis
Diagnostic imaging
Diagnostic systems
Functional magnetic resonance imaging
Health aspects
Interpersonal communication in children
Learning
Machine learning
Magnetic resonance
Magnetic resonance imaging
Medical diagnosis
Medical imaging
Methods
Neural networks
Neurodevelopmental disorders
Neuroimaging
Pervasive developmental disorders
Psychological aspects
Supervised learning
Unsupervised learning
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Summary:Autism Spectrum Disorder (ASD) is a neurodevelopmental condition whose current psychiatric diagnostic process is subjective and behavior-based. In contrast, functional magnetic resonance imaging (fMRI) can objectively measure brain activity and is useful for identifying brain disorders. However, the ASD diagnostic models employed to date have not reached satisfactory levels of accuracy. This study proposes the use of MAACNN, a method that utilizes multi-view convolutional neural networks (CNNs) in conjunction with attention mechanisms for identifying ASD in multi-scale fMRI. The proposed algorithm effectively combines unsupervised and supervised learning. In the initial stage, we employ stacked denoising autoencoders, an unsupervised learning method for feature extraction, which provides different nodes to adapt to multi-scale data. In the subsequent stage, we perform supervised learning by employing multi-view CNNs for classification and obtain the final results. Finally, multi-scale data fusion is achieved by using the attention fusion mechanism. The ABIDE dataset is used to evaluate the model we proposed., and the experimental results show that MAACNN achieves superior performance with 75.12% accuracy and 0.79 AUC on ABIDE-I, and 72.88% accuracy and 0.76 AUC on ABIDE-II. The proposed method significantly contributes to the clinical diagnosis of ASD.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0295621