A deep learning framework for accurate mammographic mass classification using local context attention module
We introduced a novel CNN-based framework that utilizes dual mammogram views for the BC classification. It utilizes LCAM, which further understands the local characteristics surrounding breast masses, aiming to enhance the accuracy and consistency of classification outcomes.
Authors
- Yassir Edrees Almalki (ORCID: https://orcid.org/0000-0001-7046-5311)
- Ali Mahmoud (ORCID: https://orcid.org/0000-0003-0757-5723)
- Rasha Karam (ORCID: https://orcid.org/0000-0001-9651-6842)
- Sharifa Khalid Alduraibi (ORCID: https://orcid.org/0000-0003-0810-9337)
- Mohammed Ghazal
- Ibrahim Abdelhalim (ORCID: https://orcid.org/0009-0000-1544-7276)
- Norah Saleh Alghamdi (ORCID: https://orcid.org/0000-0001-6421-6001)
- Mohammad Abd Alkhalik Basha (ORCID: https://orcid.org/0000-0002-9075-8020)
- Abdelrahman Abdallah (ORCID: https://orcid.org/0000-0001-8747-4927)
- Sohail G. Contractor (ORCID: https://orcid.org/0000-0002-4809-8890)
- Ayman El‐Baz (ORCID: https://orcid.org/0000-0001-7264-1323)
- Hassan Mohamed (ORCID: https://orcid.org/0000-0002-2940-9868)
Institutions
- Princess Nourah bint Abdulrahman University (SA)
- Abu Dhabi University (AE)
- University of Louisville (US)
- Qassim University (SA)
- Mansoura University (EG)
- Zagazig University (EG)
- Benha University (EG)
- McMaster University Medical Centre (CA)
- University of Louisville Hospital (US)
- University of Louisville Physicians (US)
- Najran University (SA)
- Assiut University (EG)
- McMaster University (CA)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-09-23
- DOI
- https://doi.org/10.1002/mp.18119
- Citations
- 1
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 2.48