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A Deep Neural Network-Based Model for Screening Autism Spectrum Disorder Using the Quantitative Checklist for Autism in Toddlers (QCHAT) / K. K. MUJEEB RAHMAN in Journal of Autism and Developmental Disorders, 52-6 (June 2022)
[article]
Titre : A Deep Neural Network-Based Model for Screening Autism Spectrum Disorder Using the Quantitative Checklist for Autism in Toddlers (QCHAT) Type de document : Texte imprimé et/ou numérique Auteurs : K. K. MUJEEB RAHMAN, Auteur ; M. MONICA SUBASHINI, Auteur Article en page(s) : p.2732-2746 Langues : Anglais (eng) Mots-clés : Auc Autism spectrum disorder Deep neural networks (DNN) Machine learning Qchat Qchat-10 Index. décimale : PER Périodiques Résumé : Autism spectrum disorder (ASD) is an abnormal condition of brain development characterized by impaired cognitive ability, speech and human interactions, in addition to a set of repetitive and stereotyped patterns of behaviours. Although no cure for autism exists, early medical intervention can improve the associated symptoms and quality of life. Several manually executed screening tools help to identify the ASD-related behavioural traits in the children that assists the specialist in diagnosing the disease accurately. The quantitative checklist for autism in toddlers (QCHAT) is one of the efficient screening tools used worldwide for ASD screening. ASD diagnosis requires many different manually administered procedures; hence long delay is encountered in getting final results. In recent years, deep neural network (DNN) popularity has been immensely increasing due to its supremacy in solving complex problems. The objective of this research is to apply algorithms, based on the deep neural network (DNN) to identify patients with ASD from the QCHAT datasets. We have used two datasets, the QCHAT and QCHAT-10, in our study. The results obtained show that related to contemporary techniques, the proposed method brings better performance. En ligne : http://dx.doi.org/10.1007/s10803-021-05141-2 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=474
in Journal of Autism and Developmental Disorders > 52-6 (June 2022) . - p.2732-2746[article] A Deep Neural Network-Based Model for Screening Autism Spectrum Disorder Using the Quantitative Checklist for Autism in Toddlers (QCHAT) [Texte imprimé et/ou numérique] / K. K. MUJEEB RAHMAN, Auteur ; M. MONICA SUBASHINI, Auteur . - p.2732-2746.
Langues : Anglais (eng)
in Journal of Autism and Developmental Disorders > 52-6 (June 2022) . - p.2732-2746
Mots-clés : Auc Autism spectrum disorder Deep neural networks (DNN) Machine learning Qchat Qchat-10 Index. décimale : PER Périodiques Résumé : Autism spectrum disorder (ASD) is an abnormal condition of brain development characterized by impaired cognitive ability, speech and human interactions, in addition to a set of repetitive and stereotyped patterns of behaviours. Although no cure for autism exists, early medical intervention can improve the associated symptoms and quality of life. Several manually executed screening tools help to identify the ASD-related behavioural traits in the children that assists the specialist in diagnosing the disease accurately. The quantitative checklist for autism in toddlers (QCHAT) is one of the efficient screening tools used worldwide for ASD screening. ASD diagnosis requires many different manually administered procedures; hence long delay is encountered in getting final results. In recent years, deep neural network (DNN) popularity has been immensely increasing due to its supremacy in solving complex problems. The objective of this research is to apply algorithms, based on the deep neural network (DNN) to identify patients with ASD from the QCHAT datasets. We have used two datasets, the QCHAT and QCHAT-10, in our study. The results obtained show that related to contemporary techniques, the proposed method brings better performance. En ligne : http://dx.doi.org/10.1007/s10803-021-05141-2 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=474