Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by a lack of social communication and social interaction. Autism is a mental disorder investigated by social and computational intelligence scientists utilizing advanced technologies such as machine learning models to enhance clinicians’ ability to provide robust diagnosis and prognosis of autism. However, with dynamic changes in autism behaviour patterns, these models’ quality and accuracy have become a great challenge for clinical practitioners. We applied a deep neural network learning on a large brain image dataset obtained from ABIDE (autism brain imaging data exchange) to provide an efficient diagnosis of ASD, especially for children. Our deep learning model combines unsupervised neural network learning, an autoencoder, and supervised deep learning using convolutional neural networks. Our proposed algorithm outperforms individual-based classifiers measured by various validations and assessment measures. Experimental results indicate that the autoencoder combined with the convolution neural networks provides the best performance by achieving 84.05% accuracy and Area under the Curve (AUC) value of 0.78.
Highlights
Autism spectrum disorder (ASD) is one of the brain development disorders
We focus on classifying individuals who have ASD from typically developing controls subjects using functional magnetic resonance imaging images provided by the autism brain imaging data exchange (ABIDE) [8] to study brain activities
The results show that the random forest has performed very well for the classification compared to support vector machines (SVMs) and K-nearest neighbour (KNN)
Summary
Autism spectrum disorder (ASD) is one of the brain development disorders. World Health Organization (WHO), 1 in 60 children has an autism spectrum disorder [1]. ASD is an intellectual disability; many of those on the autism spectrum have extraordinary abilities and skills. It is worth mentioning that researchers found differences in the brains of babies born before 27 weeks [4], i.e., babies born very prematurely are at higher risk for developing ASD. Many machine learning and neural network methods have recently shown an improvement in autism. We focus on classifying individuals who have ASD from typically developing controls subjects using functional magnetic resonance imaging (fMRI) images provided by the autism brain imaging data exchange (ABIDE) [8] to study brain activities.
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