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

• Published in: Multimedia tools and applications Vol. 77; no. 17; pp. 22809 - 22820
• Main Authors: Xiao, Zhiyong, Wang, Canhua, Jia, Nan, Wu, Jianhua
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2018; Springer Nature B.V
• Subjects: 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

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...