CNN-Based Handwriting Analysis for the Prediction of Autism Spectrum Disorder
Paper
Nawer, Nafisa, Parvez, Mohammad Zavid, Hossain, Muhammad Iqbal, Barua, Prabal Datta, Rahim, Mia and Chakraborty, Subrata. 2023. "CNN-Based Handwriting Analysis for the Prediction of Autism Spectrum Disorder." Second International Conference on Innovations in Computing Research (ICR’23). Madrid, Spain 04 - 06 Sep 2023 Switzerland . https://doi.org/10.1007/978-3-031-35308-6_14
| Paper/Presentation Title | CNN-Based Handwriting Analysis for the Prediction of Autism Spectrum Disorder |
|---|---|
| Presentation Type | Paper |
| Authors | Nawer, Nafisa, Parvez, Mohammad Zavid, Hossain, Muhammad Iqbal, Barua, Prabal Datta, Rahim, Mia and Chakraborty, Subrata |
| Journal or Proceedings Title | Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23) |
| Journal Citation | 721, pp. 165-174 |
| Number of Pages | 10 |
| Year | 2023 |
| Place of Publication | Switzerland |
| ISBN | 9783031353079 |
| 9783031353086 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-031-35308-6_14 |
| Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-3-031-35308-6_14 |
| Web Address (URL) of Conference Proceedings | https://link.springer.com/book/10.1007/978-3-031-35308-6 |
| Conference/Event | Second International Conference on Innovations in Computing Research (ICR’23) |
| Event Details | Second International Conference on Innovations in Computing Research (ICR’23) Delivery In person Event Date 04 to end of 06 Sep 2023 Event Location Madrid, Spain |
| 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%. |
| Keywords | ASD; InceptionV3; VGG19; Xception; ROC; AUC; kappa; Confusion matrix |
| ANZSRC Field of Research 2020 | 460912. Knowledge and information management |
| Public Notes | The accessible file is the accepted version of the paper. Please refer to the URL for the published version. |
| Series | Lecture Notes in Networks and Systems |
| Byline Affiliations | BRAC University, Bangladesh |
| Asia Pacific International College (APIC), Australia | |
| Torrens University | |
| Australian Catholic University | |
| Charles Sturt University | |
| School of Business | |
| University of New England | |
| Cogninet Australia, Australia |
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https://research.usq.edu.au/item/z276v/cnn-based-handwriting-analysis-for-the-prediction-of-autism-spectrum-disorder
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