Ghazal, T M, Alrababah, Hamza, Khan, Muhammad Adnan, Munir, Sundus, Abbas, Sagheer and Athar, Atifa Early Detection of Autism in Children Using Transfer Learning. Intelligent Automation and Soft Computing, 36 (1). pp. 11-22. ISSN 1079-8587
Full text not available from this repository.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.
Affiliation: | Skyline University College |
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SUC Author(s): | Ghazal, T M ORCID: https://orcid.org/0000-0003-0672-7924, Alrababah, Hamza and Khan, Muhammad Adnan |
All Author(s): | Ghazal, T M, Alrababah, Hamza, Khan, Muhammad Adnan, Munir, Sundus, Abbas, Sagheer and Athar, Atifa |
Item Type: | Article |
Uncontrolled Keywords: | Autism spectrum disorder, convolutional neural network, loss rate, transfer learning, AlexNet, deep learning |
Subjects: | B Information Technology > BR Deep Learning B Information Technology > BW Computer Networks |
Divisions: | Skyline University College > School of IT |
Depositing User: | Mr Mosys Team |
Date Deposited: | 25 Dec 2023 13:54 |
Last Modified: | 25 Dec 2023 13:54 |
URI: | https://research.skylineuniversity.ac.ae/id/eprint/631 |
Publisher URL: | https://www.researchgate.net/publication/364165912... |
Publisher OA policy: | https://v2.sherpa.ac.uk/id/publication/24946?templ... |
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