DETECTION OF AUTISM SPECTRUM DISORDER USING TRANSFER LEARNING.
Autism is an insidious developmental disorder exemplified by impaired development in communication and social interaction. The number of cases with autism in children and adults are increasing day by day. The causes of autism are unknown, hence the early diagnosis of autism accompanied with intensiv...
| Publicado en: | Turkish Journal of Physiotherapy Rehabilitation Vol. 32; no. 2; pp. 926 - 934 |
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| Autores principales: | , , , |
| Formato: | tables/charts Journal Article |
| Publicado: |
Turkish Journal of Physiotherapy & Rehabilitation
2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151006051&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151006051 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13008757 YU1 jtl: Turkish Journal of Physiotherapy Rehabilitation issn: 13008757 maglogo: N pubinfo: dt: 2021 vid: 32 iid: 2 pid: 20392 pub: Turkish Journal of Physiotherapy & Rehabilitation place: Kizilay/ Ankara, <Blank> artinfo: ui: 151006051 151006051 151006051 151006051 ppf: 926 ppct: 8 formats: fmt: @attributes: type: P tig: atl: DETECTION OF AUTISM SPECTRUM DISORDER USING TRANSFER LEARNING. aug: au: KALAISELVI, A. NAGARATHINAM, S. PAUL, TIMOTHY DAYAKAR ALAGUMEENAAKSHI, M. affil: Assistant Professor, Kumaraguru College of Technology, Coimbatore-641049, Tamil Nadu sug: subj: Autism Spectrum Disorder Diagnosis Machine Learning Utilization Artificial Intelligence Minimum Data Set Child Facial Expression Algorithms Image Processing, Computer Assisted Human Error Prevention and Control Neural Networks (Computer) Deep Learning Child: 6-12 years ab: Autism is an insidious developmental disorder exemplified by impaired development in communication and social interaction. The number of cases with autism in children and adults are increasing day by day. The causes of autism are unknown, hence the early diagnosis of autism accompanied with intensive treatment can make a wide behavioral change in the lives of children or adults with this disorder. With the advent of artificial intelligence this has become possible thus saving lives of many people. This paper proposes the detection of ASD in children with the help of transfer learning. The proposed methodology uses four different CNN architecture in the detection of autism namely, VGG19, Resnet50, InceptionV3 and NASNetLarge models. A dataset consisting of images of the facial expressions of children with autism and non-autism are provided as training, testing and validation data. The architecture NASNetLarge provided an accuracy of 87.50% and a loss of 0.372 compared to other three models. pubtype: Academic Journal doctype: tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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