WOAENet: a whale optimization-guided ensemble deep learning with soft voting for uterine cancer diagnosis based on MRI images
WOAENet demonstrates a high level of accuracy and reliability in classifying uterine MRI images, marking a significant advancement by utilizing a novel dataset. The findings support the potential of AI-driven approaches in enhancing the diagnosis and treatment of gynecological conditions, paving the way for more accessible and accurate clinical tools.
Authors
- Hanan Akhdar (ORCID: https://orcid.org/0000-0001-5144-7169)
- Omar F. Altal (ORCID: https://orcid.org/0000-0002-8594-219X)
- Hamad Yahia Abu Mhanna (ORCID: https://orcid.org/0000-0001-6629-3297)
- Kholoud J. Sandougah
- Salwa Jadid Alanazi
- Faheem Zayed (ORCID: https://orcid.org/0000-0002-3228-6694)
- Amer Sindiani (ORCID: https://orcid.org/0000-0001-5115-7372)
- Mohammad Amin
- Noor Alqasem
- Rola Madain (ORCID: https://orcid.org/0009-0005-7674-895X)
- Salem Alhatamleh
Institutions
- Jordan University of Science and Technology (JO)
- Imam Mohammad ibn Saud Islamic University (SA)
- Specialty Hospital, Jordan (JO)
- Yarmouk University (JO)
- Isra University (JO)
Publication Details
- Journal
- Frontiers in Artificial Intelligence
- Published
- 2025-10-20
- DOI
- https://doi.org/10.3389/frai.2025.1664201
- Citations
- 4
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 9.92