Research and Design of Autism Smart Diagnosis Information System Based on Chinese Children's Facial Expression Data and Deep Convolution Neural Network

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dc.contributor.author Zhao, Wang
dc.date.accessioned 2020-01-04T07:50:33Z
dc.date.available 2020-01-04T07:50:33Z
dc.date.issued 2020-01-07
dc.description.abstract In this paper, the standard facial expression database FER2013 and CK + are used as the main training samples for autism diagnosis model.The facial expression image data of 16 Chinese children were collected as supplementary training samples.We use deep convolution neural network VGG19 and Resnet18 artificial intelligence algorithms to research and develop an smart information system for the diagnosis of autism through facial expression data.Ten normal children and ten autistic children were recruited for the comparative test to verify the accuracy of the system.After testing, the accuracy of facial expression recognition of this system reaches 81.4%.This research is based on the actual business needs of the hospital. The system can diagnose autism as early as possible,and promote the early treatment and rehabilitation of patients, thereby reducing the economic and mental burden of patients. Therefore, this smart information system has good social benefits and application value.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2020.406
dc.identifier.isbn 978-0-9981331-3-3
dc.identifier.uri http://hdl.handle.net/10125/64148
dc.language.iso eng
dc.relation.ispartof Proceedings of the 53rd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Data-Driven Smart Health in Asia Pacific
dc.subject autism
dc.subject deep convolution neural network
dc.subject facial expression database
dc.subject smart diagnostic information system
dc.title Research and Design of Autism Smart Diagnosis Information System Based on Chinese Children's Facial Expression Data and Deep Convolution Neural Network
dc.type Conference Paper
dc.type.dcmi Text
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