Single Volume Image Generator and Deep Learning-Based ASD Classification

计算机科学 人工智能 卷积神经网络 深度学习 模式识别(心理学) 分类器(UML) 体素 上下文图像分类 自闭症谱系障碍 预处理器 功能磁共振成像 机器学习 神经影像学 图像(数学) 自闭症 心理学 精神科 发展心理学 生物 神经科学
作者
Md Rishad Ahmed,Yuan Zhang,Yi Liu,Hongen Liao
出处
期刊:IEEE Journal of Biomedical and Health Informatics [Institute of Electrical and Electronics Engineers]
卷期号:24 (11): 3044-3054 被引量:57
标识
DOI:10.1109/jbhi.2020.2998603
摘要

Autism spectrum disorder (ASD) is an intricate neuropsychiatric brain disorder characterized by social deficits and repetitive behaviors. Deep learning approaches have been applied in clinical or behavioral identification of ASD; most erstwhile models are inadequate in their capacity to exploit the data richness. On the other hand, classification techniques often solely rely on region-based summary and/or functional connectivity analysis of functional magnetic resonance imaging (fMRI). Besides, biomedical data modeling to analyze big data related to ASD is still perplexing due to its complexity and heterogeneity. Single volume image consideration has not been previously investigated in classification purposes. By deeming these challenges, in this work, firstly, we design an image generator to generate single volume brain images from the whole-brain image by considering the voxel time point of each subject separately. Then, to classify ASD and typical control participants, we evaluate four deep learning approaches with their corresponding ensemble classifiers comprising one amended Convolutional Neural Network (CNN). Finally, to check out the data variability, we apply the proposed CNN classifier with leave-one-site-out 5-fold cross-validation across the sites and validate our findings by comparing with literature reports. We showcase our approach on large-scale multi-site brain imaging dataset (ABIDE) by considering four preprocessing pipelines, which outperforms the state-of-the-art methods. Hence, it is robust and consistent.
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