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A face image classification method of autistic children based on the two-phase transfer learning

Autism spectrum disorder (ASD) is a neurodevelopmental disorder, which seriously affects children’s normal life. Screening potential autistic children before professional diagnose is helpful to early detection and early intervention. Autistic children have some different facial features from non-aut...

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Autores principales: Li, Ying, Huang, Wen-Cong, Song, Pei-Hua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10501480/
https://www.ncbi.nlm.nih.gov/pubmed/37720633
http://dx.doi.org/10.3389/fpsyg.2023.1226470
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author Li, Ying
Huang, Wen-Cong
Song, Pei-Hua
author_facet Li, Ying
Huang, Wen-Cong
Song, Pei-Hua
author_sort Li, Ying
collection PubMed
description Autism spectrum disorder (ASD) is a neurodevelopmental disorder, which seriously affects children’s normal life. Screening potential autistic children before professional diagnose is helpful to early detection and early intervention. Autistic children have some different facial features from non-autistic children, so the potential autistic children can be screened by taking children’s facial images and analyzing them with a mobile phone. The area under curve (AUC) is a more robust metrics than accuracy in evaluating the performance of a model used to carry out the two-category classification, and the AUC of the deep learning model suitable for the mobile terminal in the existing research can be further improved. Moreover, the size of an input image is large, which is not fit for a mobile phone. A deep transfer learning method is proposed in this research, which can use images with smaller size and improve the AUC of existing studies. The proposed transfer method uses the two-phase transfer learning mode and the multi-classifier integration mode. For MobileNetV2 and MobileNetV3-Large that are suitable for a mobile phone, the two-phase transfer learning mode is used to improve their classification performance, and then the multi-classifier integration mode is used to integrate them to further improve the classification performance. A multi-classifier integrating calculation method is also proposed to calculate the final classification results according to the classifying results of the participating models. The experimental results show that compared with the one-phase transfer learning, the two-phase transfer learning can significantly improve the classification performance of MobileNetV2 and MobileNetV3-Large, and the classification performance of the integrated classifier is better than that of any participating classifiers. The accuracy of the integrated classifier in this research is 90.5%, and the AUC is 96.32%, which is 3.51% greater than the AUC (92.81%) of the previous studies.
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spelling pubmed-105014802023-09-15 A face image classification method of autistic children based on the two-phase transfer learning Li, Ying Huang, Wen-Cong Song, Pei-Hua Front Psychol Psychology Autism spectrum disorder (ASD) is a neurodevelopmental disorder, which seriously affects children’s normal life. Screening potential autistic children before professional diagnose is helpful to early detection and early intervention. Autistic children have some different facial features from non-autistic children, so the potential autistic children can be screened by taking children’s facial images and analyzing them with a mobile phone. The area under curve (AUC) is a more robust metrics than accuracy in evaluating the performance of a model used to carry out the two-category classification, and the AUC of the deep learning model suitable for the mobile terminal in the existing research can be further improved. Moreover, the size of an input image is large, which is not fit for a mobile phone. A deep transfer learning method is proposed in this research, which can use images with smaller size and improve the AUC of existing studies. The proposed transfer method uses the two-phase transfer learning mode and the multi-classifier integration mode. For MobileNetV2 and MobileNetV3-Large that are suitable for a mobile phone, the two-phase transfer learning mode is used to improve their classification performance, and then the multi-classifier integration mode is used to integrate them to further improve the classification performance. A multi-classifier integrating calculation method is also proposed to calculate the final classification results according to the classifying results of the participating models. The experimental results show that compared with the one-phase transfer learning, the two-phase transfer learning can significantly improve the classification performance of MobileNetV2 and MobileNetV3-Large, and the classification performance of the integrated classifier is better than that of any participating classifiers. The accuracy of the integrated classifier in this research is 90.5%, and the AUC is 96.32%, which is 3.51% greater than the AUC (92.81%) of the previous studies. Frontiers Media S.A. 2023-08-31 /pmc/articles/PMC10501480/ /pubmed/37720633 http://dx.doi.org/10.3389/fpsyg.2023.1226470 Text en Copyright © 2023 Li, Huang and Song. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Li, Ying
Huang, Wen-Cong
Song, Pei-Hua
A face image classification method of autistic children based on the two-phase transfer learning
title A face image classification method of autistic children based on the two-phase transfer learning
title_full A face image classification method of autistic children based on the two-phase transfer learning
title_fullStr A face image classification method of autistic children based on the two-phase transfer learning
title_full_unstemmed A face image classification method of autistic children based on the two-phase transfer learning
title_short A face image classification method of autistic children based on the two-phase transfer learning
title_sort face image classification method of autistic children based on the two-phase transfer learning
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10501480/
https://www.ncbi.nlm.nih.gov/pubmed/37720633
http://dx.doi.org/10.3389/fpsyg.2023.1226470
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