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Improved Transfer-Learning-Based Facial Recognition Framework to Detect Autistic Children at an Early Stage
Autism spectrum disorder (ASD) is a complex neuro-developmental disorder that affects social skills, language, speech and communication. Early detection of ASD individuals, especially children, could help to devise and strategize right therapeutic plan at right time. Human faces encode important mar...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8230000/ https://www.ncbi.nlm.nih.gov/pubmed/34073085 http://dx.doi.org/10.3390/brainsci11060734 |
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author | Akter, Tania Ali, Mohammad Hanif Khan, Md. Imran Satu, Md. Shahriare Uddin, Md. Jamal Alyami, Salem A. Ali, Sarwar Azad, AKM Moni, Mohammad Ali |
author_facet | Akter, Tania Ali, Mohammad Hanif Khan, Md. Imran Satu, Md. Shahriare Uddin, Md. Jamal Alyami, Salem A. Ali, Sarwar Azad, AKM Moni, Mohammad Ali |
author_sort | Akter, Tania |
collection | PubMed |
description | Autism spectrum disorder (ASD) is a complex neuro-developmental disorder that affects social skills, language, speech and communication. Early detection of ASD individuals, especially children, could help to devise and strategize right therapeutic plan at right time. Human faces encode important markers that can be used to identify ASD by analyzing facial features, eye contact, and so on. In this work, an improved transfer-learning-based autism face recognition framework is proposed to identify kids with ASD in the early stages more precisely. Therefore, we have collected face images of children with ASD from the Kaggle data repository, and various machine learning and deep learning classifiers and other transfer-learning-based pre-trained models were applied. We observed that our improved MobileNet-V1 model demonstrates the best accuracy of 90.67% and the lowest 9.33% value of both fall-out and miss rate compared to the other classifiers and pre-trained models. Furthermore, this classifier is used to identify different ASD groups investigating only autism image data using k-means clustering technique. Thus, the improved MobileNet-V1 model showed the highest accuracy (92.10%) for k = 2 autism sub-types. We hope this model will be useful for physicians to detect autistic children more explicitly at the early stage. |
format | Online Article Text |
id | pubmed-8230000 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-82300002021-06-26 Improved Transfer-Learning-Based Facial Recognition Framework to Detect Autistic Children at an Early Stage Akter, Tania Ali, Mohammad Hanif Khan, Md. Imran Satu, Md. Shahriare Uddin, Md. Jamal Alyami, Salem A. Ali, Sarwar Azad, AKM Moni, Mohammad Ali Brain Sci Article Autism spectrum disorder (ASD) is a complex neuro-developmental disorder that affects social skills, language, speech and communication. Early detection of ASD individuals, especially children, could help to devise and strategize right therapeutic plan at right time. Human faces encode important markers that can be used to identify ASD by analyzing facial features, eye contact, and so on. In this work, an improved transfer-learning-based autism face recognition framework is proposed to identify kids with ASD in the early stages more precisely. Therefore, we have collected face images of children with ASD from the Kaggle data repository, and various machine learning and deep learning classifiers and other transfer-learning-based pre-trained models were applied. We observed that our improved MobileNet-V1 model demonstrates the best accuracy of 90.67% and the lowest 9.33% value of both fall-out and miss rate compared to the other classifiers and pre-trained models. Furthermore, this classifier is used to identify different ASD groups investigating only autism image data using k-means clustering technique. Thus, the improved MobileNet-V1 model showed the highest accuracy (92.10%) for k = 2 autism sub-types. We hope this model will be useful for physicians to detect autistic children more explicitly at the early stage. MDPI 2021-05-31 /pmc/articles/PMC8230000/ /pubmed/34073085 http://dx.doi.org/10.3390/brainsci11060734 Text en © 2021 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 Akter, Tania Ali, Mohammad Hanif Khan, Md. Imran Satu, Md. Shahriare Uddin, Md. Jamal Alyami, Salem A. Ali, Sarwar Azad, AKM Moni, Mohammad Ali Improved Transfer-Learning-Based Facial Recognition Framework to Detect Autistic Children at an Early Stage |
title | Improved Transfer-Learning-Based Facial Recognition Framework to Detect Autistic Children at an Early Stage |
title_full | Improved Transfer-Learning-Based Facial Recognition Framework to Detect Autistic Children at an Early Stage |
title_fullStr | Improved Transfer-Learning-Based Facial Recognition Framework to Detect Autistic Children at an Early Stage |
title_full_unstemmed | Improved Transfer-Learning-Based Facial Recognition Framework to Detect Autistic Children at an Early Stage |
title_short | Improved Transfer-Learning-Based Facial Recognition Framework to Detect Autistic Children at an Early Stage |
title_sort | improved transfer-learning-based facial recognition framework to detect autistic children at an early stage |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8230000/ https://www.ncbi.nlm.nih.gov/pubmed/34073085 http://dx.doi.org/10.3390/brainsci11060734 |
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