Automatic segmentation, classification, and prediction of pelvic bone tumors using deep learning techniques

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Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: 1 Online-Ressource.

Language: Englisch

Bibliographic citation: Automatic segmentation, classification, and prediction of pelvic bone tumors using deep learning techniques ; volume:71 ; number:1 ; day:26 ; month:11 ; year:2024 ; pages:1-22 ; date:12.2024
Journal of engineering and applied science ; 71, Heft 1 (26.11.2024), 1-22, 12.2024

Creator: Shouman, Mona
Rahouma, Kamel Hussein
Hamed, Hesham F. A.

Contributor: SpringerLink (Online service)

DOI: 10.1186/s44147-024-00551-2

URN: urn:nbn:de:101:1-2502112149009.843447368028

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 5:31 AM UTC

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Shouman, Mona
Rahouma, Kamel Hussein
Hamed, Hesham F. A.
SpringerLink (Online service)

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Automatic segmentation, classification, and prediction of pelvic bone tumors using deep learning techniques

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