Identification of Autism Spectrum Disorder Using Topological Data Analysis.
Autism spectrum disorder (ASD) is a pervasive brain development disease. Recently, the incidence rate of ASD has increased year by year and posed a great threat to the lives and families of individuals with ASD. Therefore, the study of ASD has become very important. A suitable feature representation...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 3; pp. 1023 - 1038 |
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| Autores principales: | , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2024
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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=178678166&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 178678166 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2024 vid: 37 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 178678166 178678166 178678166 10.1007/s10278-024-01002-3 178678166 ppf: 1023 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Identification of Autism Spectrum Disorder Using Topological Data Analysis. aug: au: Zhang, Xudong Gao, Yaru Zhang, Yunge Li, Fengling Li, Huanjie Lei, Fengchun affil: https://ror.org/023hj5876 School of Mathematical Sciences, Dalian University of Technology, 116024, Dalian, China sug: subj: Autism Spectrum Disorder Diagnosis Data Analysis, Statistical Human Funding Source Mathematics Bar Coding Descriptive Statistics Sensitivity and Specificity Machine Learning Resource Databases ab: Autism spectrum disorder (ASD) is a pervasive brain development disease. Recently, the incidence rate of ASD has increased year by year and posed a great threat to the lives and families of individuals with ASD. Therefore, the study of ASD has become very important. A suitable feature representation that preserves the data intrinsic information and also reduces data complexity is very vital to the performance of established models. Topological data analysis (TDA) is an emerging and powerful mathematical tool for characterizing shapes and describing intrinsic information in complex data. In TDA, persistence barcodes or diagrams are usually regarded as visual representations of topological features of data. In this paper, the Regional Homogeneity (ReHo) data of subjects obtained from Autism Brain Imaging Data Exchange (ABIDE) database were used to extract features by using TDA. The average accuracy of cross validation on ABIDE I database was 95.6% that was higher than any other existing methods (the highest accuracy among existing methods was 93.59%). The average accuracy for sampling with the same resolutions with the ABIDE I on the ABIDE II database was 96.5% that was also higher than any other existing methods (the highest accuracy among existing methods was 75.17%). pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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