Comparison of Neural Networks' Performance in Early Screening of Autism Spectrum Disorders Under Two MRI Principles

• Published in: 2019 International Conference on Networking and Network Applications (NaNA) pp. 338 - 343
• Main Authors: Zhang, Mingkang, Ma, Yanbiao, Zheng, Linan, Wang, Yuanyuan, Liu, Zhihong, Ma, Jianfeng
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2019
• Subjects: 3D Convolutional Neural Network (3D CNN); Autism; Autism Spectrum Disorder (ASD); Biological neural networks; Convolution; Convolutional Long Short-Term Memory (ConvLSTM); Feature extraction; Functional magnetic resonance imaging; Functional Magnetic Resonance Imaging (fMRI); Structural Magnetic Resonance Imaging (sMRI); Three-dimensional displays
• DOI: 10.1109/NaNA.2019.00065

Autism spectrum disorder (ASD) is a congenital neurodevelopmental disorder caused by a disorder of the nervous system. ASD is mainly characterized by developmental disorders, as well as abnormalities in social skills, communication skills, interests, and behavioral patterns. ASD is a congenital developmental disorder that cannot be completely cured by existing medical methods, and only after-day intervention can relieve its symptoms. In addition, the sooner you get the intervention, the better the patient's recovery. However, when most patients with ASD show the symptoms, they have missed the best intervention period. In order to allow the subject to be diagnosed earlier with ASD, we applied deep learning techniques to evaluate neural networks in ASD under functional magnetic resonance imaging (fMRI) imaging. At the same time, we also compared the accuracy of detection under the principle of structural magnetic resonance imaging (sMRI) imaging. Our experiments show that neural networks perform much better on fMRI data than sMRI data.