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Identification of Autism in Children Using Static Facial Features and Deep Neural Networks
Autism spectrum disorder (ASD) is a complicated neurological developmental disorder that manifests itself in a variety of ways. The child diagnosed with ASD and their parents’ daily lives can be dramatically improved with early diagnosis and appropriate medical intervention. The applicability of sta...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8773918/ https://www.ncbi.nlm.nih.gov/pubmed/35053837 http://dx.doi.org/10.3390/brainsci12010094 |
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author | Mujeeb Rahman, K. K. Subashini, M. Monica |
author_facet | Mujeeb Rahman, K. K. Subashini, M. Monica |
author_sort | Mujeeb Rahman, K. K. |
collection | PubMed |
description | Autism spectrum disorder (ASD) is a complicated neurological developmental disorder that manifests itself in a variety of ways. The child diagnosed with ASD and their parents’ daily lives can be dramatically improved with early diagnosis and appropriate medical intervention. The applicability of static features extracted from autistic children’s face photographs as a biomarker to distinguish them from typically developing children is investigated in this study paper. We used five pre-trained CNN models: MobileNet, Xception, EfficientNetB0, EfficientNetB1, and EfficientNetB2 as feature extractors and a DNN model as a binary classifier to identify autism in children accurately. We used a publicly available dataset to train the suggested models, which consisted of face pictures of children diagnosed with autism and controls classed as autistic and non-autistic. The Xception model outperformed the others, with an AUC of 96.63%, a sensitivity of 88.46%, and an NPV of 88%. EfficientNetB0 produced a consistent prediction score of 59% for autistic and non-autistic groups with a 95% confidence level. |
format | Online Article Text |
id | pubmed-8773918 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87739182022-01-21 Identification of Autism in Children Using Static Facial Features and Deep Neural Networks Mujeeb Rahman, K. K. Subashini, M. Monica Brain Sci Article Autism spectrum disorder (ASD) is a complicated neurological developmental disorder that manifests itself in a variety of ways. The child diagnosed with ASD and their parents’ daily lives can be dramatically improved with early diagnosis and appropriate medical intervention. The applicability of static features extracted from autistic children’s face photographs as a biomarker to distinguish them from typically developing children is investigated in this study paper. We used five pre-trained CNN models: MobileNet, Xception, EfficientNetB0, EfficientNetB1, and EfficientNetB2 as feature extractors and a DNN model as a binary classifier to identify autism in children accurately. We used a publicly available dataset to train the suggested models, which consisted of face pictures of children diagnosed with autism and controls classed as autistic and non-autistic. The Xception model outperformed the others, with an AUC of 96.63%, a sensitivity of 88.46%, and an NPV of 88%. EfficientNetB0 produced a consistent prediction score of 59% for autistic and non-autistic groups with a 95% confidence level. MDPI 2022-01-12 /pmc/articles/PMC8773918/ /pubmed/35053837 http://dx.doi.org/10.3390/brainsci12010094 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Mujeeb Rahman, K. K. Subashini, M. Monica Identification of Autism in Children Using Static Facial Features and Deep Neural Networks |
title | Identification of Autism in Children Using Static Facial Features and Deep Neural Networks |
title_full | Identification of Autism in Children Using Static Facial Features and Deep Neural Networks |
title_fullStr | Identification of Autism in Children Using Static Facial Features and Deep Neural Networks |
title_full_unstemmed | Identification of Autism in Children Using Static Facial Features and Deep Neural Networks |
title_short | Identification of Autism in Children Using Static Facial Features and Deep Neural Networks |
title_sort | identification of autism in children using static facial features and deep neural networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8773918/ https://www.ncbi.nlm.nih.gov/pubmed/35053837 http://dx.doi.org/10.3390/brainsci12010094 |
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