Uterine cancer classification from CT images using convolutional feature extraction and transformer-based self-attention.

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Bibliographic Details
Title: Uterine cancer classification from CT images using convolutional feature extraction and transformer-based self-attention.
Authors: Alshdaifat EH; Department of Obstetrics and Gynecology, Faculty of Medicine, Yarmouk University, Irbid, Jordan., Sindiani AM; Department of Obstetrics and Gynecology, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan., Alhatamleh S; Computer Science Department, Faculty of Information Technology and Computer Sciences, Yarmouk University, Irbid, Jordan., Malkawi R; Department of Information Systems, Faculty of Information Technology and Computer Science, Yarmouk University, Irbid, Jordan., Madain R; Department of Obstetrics and Gynecology, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan., Almahmoud RE; Department of Obstetrics and Gynecology, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan., Al-Smadi B; Computer Science Department, Faculty of Information Technology and Computer Sciences, Yarmouk University, Irbid, Jordan., Al-Mnayyis AM; Department of Internal Medicine, College of Medicine, Yarmouk University, Irbid, Jordan., Amin M; Computer Science Department, Faculty of Information Technology and Computer Sciences, Yarmouk University, Irbid, Jordan., Abd-Alrazaq A; AI Center for Precision Health, Weill Cornell Medicine-Qatar, Qatar Foundation, Doha, Qatar.
Source: Frontiers in medicine [Front Med (Lausanne)] 2026 Feb 25; Vol. 13, pp. 1781499. Date of Electronic Publication: 2026 Feb 25 (Print Publication: 2026).
Publication Type: Journal Article
Journal Info: Publisher: Frontiers Media S.A Country of Publication: Switzerland NLM ID: 101648047 Publication Model: eCollection Cited Medium: Print ISSN: 2296-858X (Print) Linking ISSN: 2296858X NLM ISO Abbreviation: Front Med (Lausanne) Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
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Description
ISSN:2296-858X
DOI:10.3389/fmed.2026.1781499