Uterine cancer classification from CT images using convolutional feature extraction and transformer-based self-attention
The results confirm the effectiveness of integrating convolutional neural networks with transformer-based self-attention mechanisms for significantly improving uterine cancer classification from computed tomography images. The proposed model shows strong potential as a computer-aided decision-support tool for radiologists to assist in the detection of uterine cancer and may be extended to various real-world clinical applications.
Authors
- Amer Sindiani (ORCID: https://orcid.org/0000-0001-5115-7372)
- Asma'a Mohammad Al-Mnayyis
- Alaa Abd‐Alrazaq (ORCID: https://orcid.org/0000-0001-7695-4626)
- Eman Hussein Alshdaifat
- Rola Madain (ORCID: https://orcid.org/0009-0005-7674-895X)
- Rawan Eimad Almahmoud
- Salem Alhatamleh
- Mohammad Amin
- Bara'a Al-Smadi
- Rami Malkawi
Institutions
- Jordan University of Science and Technology (JO)
- Al Yarmouk University College (IQ)
- Yarmouk Private University (SY)
- Weill Cornell Medical College in Qatar (QA)
- Yarmouk University (JO)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2026-02-25
- DOI
- https://doi.org/10.3389/fmed.2026.1781499
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Weill Cornell Medical College
- Qatar National Library