Next Article in Journal
Cognitive Assessment and Rehabilitation for Pediatric-Onset Multiple Sclerosis: A Scoping Review
Next Article in Special Issue
A Complementary Sensory Tool for Children with Autism Spectrum Disorders
Previous Article in Journal
Preliminary Study on the Echo-Assisted Intersphincteric Autologous Microfragmented Adipose Tissue Injection to Control Fecal Incontinence in Children Operated for Anorectal Malformations
Previous Article in Special Issue
Preschool Teachers’ Beliefs towards Children with Autism Spectrum Disorder (ASD) in Yemen
Article

An Autoencoder-Based Deep Learning Classifier for Efficient Diagnosis of Autism

Department of Electrical, Computer, and Biomedical Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada
*
Author to whom correspondence should be addressed.
Received: 25 September 2020 / Revised: 5 October 2020 / Accepted: 8 October 2020 / Published: 14 October 2020
(This article belongs to the Special Issue New Research in Children with Neurodevelopmental Disorders)
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. View Full-Text
Keywords: autism; diagnosis; autoencoder; convolution neural network; machine learning autism; diagnosis; autoencoder; convolution neural network; machine learning
Show Figures

Figure 1

MDPI and ACS Style

Sewani, H.; Kashef, R. An Autoencoder-Based Deep Learning Classifier for Efficient Diagnosis of Autism. Children 2020, 7, 182. https://0-doi-org.brum.beds.ac.uk/10.3390/children7100182

AMA Style

Sewani H, Kashef R. An Autoencoder-Based Deep Learning Classifier for Efficient Diagnosis of Autism. Children. 2020; 7(10):182. https://0-doi-org.brum.beds.ac.uk/10.3390/children7100182

Chicago/Turabian Style

Sewani, Harshini, and Rasha Kashef. 2020. "An Autoencoder-Based Deep Learning Classifier for Efficient Diagnosis of Autism" Children 7, no. 10: 182. https://0-doi-org.brum.beds.ac.uk/10.3390/children7100182

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop