Quantifying features from X-ray images to assess early stage knee osteoarthritis.
| Publicado en: | Medical & Biological Engineering & Computing Vol. 63; no. 11; pp. 3399 - 3420 |
|---|---|
| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Nov2025
|
| 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=189590719&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 189590719 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Nov2025 vid: 63 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 189590719 186370783 10.1007/s11517-025-03405-y 189590719 ppf: 3399 ppct: 21 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Quantifying features from X-ray images to assess early stage knee osteoarthritis. aug: au: Helaly, Tanjina Faisal, Tanvir R. Moni, Ahmed Suparno Bahar Naznin, Mahmuda affil: https://ror.org/05a1qpv97 Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh sug: pubtype: Academic Journal doctype: Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
|---|