Open Access

ARTICLE

Early Detection of Autism in Children Using Transfer Learning

Taher M. Ghazal^1,2, Sundus Munir^3,4, Sagheer Abbas³, Atifa Athar⁵, Hamza Alrababah¹, Muhammad Adnan Khan^6,*

1 School of Information Technology, Skyline University College, Sharjah, 1797, UAE
2 Network and Communication Technology Lab, Center for Cyber Security, Faculty of Information Science and Technology Universiti Kebangsaan Malaysia, 43600, Malaysia
3 School of Computer Science, National College of Business Administration & Economics, Lahore, 54000, Pakistan
4 Lahore Garrison University, Lahore, 54000, Pakistan
5 Department of Computer Science, COMSATS University Islamabad–Lahore Campus, Lahore, 54000, Pakistan
6 Department of Software, Pattern Recognition and Machine Learning Lab, Gachon University, Seongnam, Gyeonggido, 13120, Korea

* Corresponding Author: Muhammad Adnan Khan. Email:

Intelligent Automation & Soft Computing 2023, 36(1), 11-22. https://doi.org/10.32604/iasc.2023.030125

Received 18 March 2022; Accepted 30 May 2022; Issue published 29 September 2022

Abstract

Autism spectrum disorder (ASD) is a challenging and complex neuro-development syndrome that affects the child’s language, speech, social skills, communication skills, and logical thinking ability. The early detection of ASD is essential for delivering effective, timely interventions. Various facial features such as a lack of eye contact, showing uncommon hand or body movements, babbling or talking in an unusual tone, and not using common gestures could be used to detect and classify ASD at an early stage. Our study aimed to develop a deep transfer learning model to facilitate the early detection of ASD based on facial features. A dataset of facial images of autistic and non-autistic children was collected from the Kaggle data repository and was used to develop the transfer learning AlexNet (ASDDTLA) model. Our model achieved a detection accuracy of 87.7% and performed better than other established ASD detection models. Therefore, this model could facilitate the early detection of ASD in clinical practice.

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Keywords

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