SAE-based classification of school-aged children with autism spectrum disorders using functional magnetic resonance imaging

This paper employs a novel-deep learning method and brain frequencies to discriminate school-aged children with autism spectrum disorders (ASD) from typically developing (TD) school-aged children with functional magnetic resonance imaging (fMRI) data of 84 subjects from the ABIDE (Autism Brain Imagi...

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Veröffentlicht in:	Multimedia tools and applications Jg. 77; H. 17; S. 22809 - 22820
Hauptverfasser:	Xiao, Zhiyong, Wang, Canhua, Jia, Nan, Wu, Jianhua
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.09.2018 Springer Nature B.V
Schlagworte:	Autism Autistic children Brain Children Classification Computer Communication Networks Computer Science Data exchange Data Structures and Information Theory Deep learning Disorders Machine learning Magnetic resonance imaging Multimedia Information Systems NMR Nuclear magnetic resonance Special Purpose and Application-Based Systems Subtraction Stacked autoencoder School-aged children Autism spectrum disorder Classification Brain frequency
ISSN:	1380-7501, 1573-7721
Online-Zugang:	Volltext
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