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ASDNet: A robust involution-based architecture for diagnosis of autism spectrum disorder utilising eye-tracking technology

Published version
Peer-reviewed

Repository DOI


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Authors

Yousuf, MA 
Nashiry, MA 
Azad, AKM 
Alyami, SA 

Abstract

jats:titleAbstract</jats:title>jats:pAutism Spectrum Disorder (ASD) is a chronic condition characterised by impairments in social interaction and communication. Early detection of ASD is desired, and there exists a demand for the development of diagnostic aids to facilitate this. A lightweight Involutional Neural Network (INN) architecture has been developed to diagnose ASD. The model follows a simpler architectural design and has less number of parameters than the state‐of‐the‐art (SOTA) image classification models, requiring lower computational resources. The proposed model is trained to detect ASD from eye‐tracking scanpath (SP), heatmap (HM), and fixation map (FM) images. Monte Carlo Dropout has been applied to the model to perform an uncertainty analysis and ensure the effectiveness of the output provided by the proposed INN model. The model has been trained and evaluated using two publicly accessible datasets. From the experiment, it is seen that the model has achieved 98.12% accuracy, 96.83% accuracy, and 97.61% accuracy on SP, FM, and HM, respectively, which outperforms the current SOTA image classification models and other existing works conducted on this topic.</jats:p>

Description

Publication status: Published

Keywords

46 Information and Computing Sciences, 4611 Machine Learning, Mental Health, Pediatric, Bioengineering, Autism, Brain Disorders, Intellectual and Developmental Disabilities (IDD)

Journal Title

IET Computer Vision

Conference Name

Journal ISSN

1751-9632
1751-9640

Volume Title

Publisher

Institution of Engineering and Technology (IET)
Sponsorship
Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) (IMSIU‐RP23004)