FACNN: fuzzy-based adaptive convolution neural network for classifying COVID-19 in noisy CXR images.
| Publicado en: | Medical & Biological Engineering & Computing Vol. 62; no. 9; pp. 2893 - 2910 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2024
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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=179087857&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179087857 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Sep2024 vid: 62 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 179087857 177059500 10.1007/s11517-024-03107-x 179087857 ppf: 2893 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: FACNN: fuzzy-based adaptive convolution neural network for classifying COVID-19 in noisy CXR images. aug: au: S., Suganyadevi V., Seethalakshmi affil: https://ror.org/02q9f3a53 Department of ECE, KPR Institute of Engineering and Technology, 641 407, Coimbatore, India sug: pubtype: Academic Journal doctype: Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
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