Automated pulmonary nodule classification from low-dose CT images using ERBNet: an ensemble learning approach.
| Publicado en: | Medical & Biological Engineering & Computing Vol. 63; no. 9; pp. 2767 - 2780 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2025
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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=187673852&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187673852 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Sep2025 vid: 63 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187673852 184488128 10.1007/s11517-025-03358-2 187673852 ppf: 2767 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automated pulmonary nodule classification from low-dose CT images using ERBNet: an ensemble learning approach. aug: au: Ahmadyar, Yashar Kamali-Asl, Alireza Samimi, Rezvan Arabi, Hossein Zaidi, Habib affil: https://ror.org/0091vmj44 Department of Medical Radiation Engineering, Shahid Beheshti University, Tehran, Iran sug: pubtype: Academic Journal doctype: Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
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