Automatic detection of autism spectrum disorder (ASD) in children using structural magnetic resonance imaging with machine vision system

Abstract

Background: Autism spectrum disorder (ASD) is a group of developmental disorders of the nervous system whose main manifestations are defects in social interactions, communication, repetitive behaviors, and limited interests. Over the years, the use of magnetic resonance imaging (MRI) to help identify patterns that are common in people with autism has increased for classification purposes. This study propose a method for classifying ASD patients versus controls using structural MRI information. In order to increase the accuracy of this method, the volume and surface features of the structural images are used simultaneously. Results: The accuracy of diagnosis respectively was 86.29%, 71.15%, 86.53%, and 88.46% with SVM, RF, KNN, and ANN classifiers. The highest accuracy of diagnosis was obtained using ANN. Conclusions: Since clinical evaluations for the diagnosis of autism are extremely time-consuming and depend on the expertise of a specialist, the importance of intelligent diagnosis of this disorder becomes clear. The aim of this study was to design an intelligent system to diagnose autism spectrum disorder.