CNN-Based Handwriting Analysis for the Prediction of Autism Spectrum Disorder

Nawer, N; Parvez, MZ; Hossain, MI; Barua, PD; Rahim, M; Chakraborty, S

CNN-Based Handwriting Analysis for the Prediction of Autism Spectrum Disorder

Nawer, N Parvez, MZ Hossain, MI Barua, PD Rahim, M Chakraborty, S

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Publisher:: Springer Nature
Publication Type:: Chapter
Citation:: Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23), 2023, 721 LNNS, pp. 165-174
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Nawer, N
dc.contributor.author	Parvez, MZ
dc.contributor.author	Hossain, MI
dc.contributor.author	Barua, PD
dc.contributor.author	Rahim, M
dc.contributor.author	Chakraborty, S https://orcid.org/0000-0002-0102-5424
dc.date.accessioned	2023-11-09T00:32:47Z
dc.date.available	2023-11-09T00:32:47Z
dc.date.issued	2023-01-01
dc.identifier.citation	Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23), 2023, 721 LNNS, pp. 165-174
dc.identifier.isbn	9783031353079
dc.identifier.uri	http://hdl.handle.net/10453/173253
dc.description.abstract	Approximately 1 in 44 children worldwide has been identified as having Autism Spectrum Disorder (ASD), according to the Centers for Disease Control and Prevention (CDC). The term ‘ASD’ is used to characterize a collection of repetitive sensory-motor activities with strong hereditary foundations. Children with autism have a higher-than-average rate of motor impairments, which causes them to struggle with handwriting. Therefore, they generally perform worse on handwriting tasks compared to typically developing children of the same age. As a result, the purpose of this research is to identify autistic children by a comparison of their handwriting to that of typically developing children. Consequently, we investigated state-of-the-art methods for identifying ASD and evaluated whether or not handwriting might serve as bio-markers for ASD modeling. In this context, we presented a novel dataset comprised of the handwritten texts of children aged 7 to 10. Additionally, three pre-trained Transfer Learning frameworks: InceptionV3, VGG19, Xception were applied to achieve the best level of accuracy possible. We have evaluated the models on a number of quantitative performance evaluation metrics and demonstrated that Xception shows the best outcome with an accuracy of 98%.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23)
dc.relation.ispartofseries	Lecture Notes in Networks and Systems
dc.relation.isbasedon	10.1007/978-3-031-35308-6_14
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	CNN-Based Handwriting Analysis for the Prediction of Autism Spectrum Disorder
dc.type	Chapter
utslib.citation.volume	721 LNNS
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2023-11-09T00:32:45Z
pubs.publication-status	Published
pubs.volume	721 LNNS

Abstract:

Approximately 1 in 44 children worldwide has been identified as having Autism Spectrum Disorder (ASD), according to the Centers for Disease Control and Prevention (CDC). The term ‘ASD’ is used to characterize a collection of repetitive sensory-motor activities with strong hereditary foundations. Children with autism have a higher-than-average rate of motor impairments, which causes them to struggle with handwriting. Therefore, they generally perform worse on handwriting tasks compared to typically developing children of the same age. As a result, the purpose of this research is to identify autistic children by a comparison of their handwriting to that of typically developing children. Consequently, we investigated state-of-the-art methods for identifying ASD and evaluated whether or not handwriting might serve as bio-markers for ASD modeling. In this context, we presented a novel dataset comprised of the handwritten texts of children aged 7 to 10. Additionally, three pre-trained Transfer Learning frameworks: InceptionV3, VGG19, Xception were applied to achieve the best level of accuracy possible. We have evaluated the models on a number of quantitative performance evaluation metrics and demonstrated that Xception shows the best outcome with an accuracy of 98%.

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http://hdl.handle.net/10453/173253