Fusion of genomic and pathological data for breast cancer detection using BCDNN
The BCDNN model shows significant promise in supporting clinical decision-making for breast cancer diagnosis. Future work may enhance model generalizability and explore integration with real-time diagnostic systems, contributing to better health outcomes for women globally. The code for this study is available on GitHub.
Authors
- Mubarak Albathan (ORCID: https://orcid.org/0000-0001-7205-8373)
- Muhammad Shafiq (ORCID: https://orcid.org/0000-0002-3724-6528)
- Riyad Almakki
- Sobia Wassan (ORCID: https://orcid.org/0000-0001-5504-7496)
- Waeal J. Obidallah
- Anas Bilal
- Zeyad Alshaikh
Institutions
- Hainan Normal University (CN)
- Islamic University (BD)
- Shandong Xiehe University (CN)
- Hubei Urban Construction Vocational and Technological College (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2026-02-18
- DOI
- https://doi.org/10.3389/fmed.2026.1726223
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00