Multi-class brain tumor MRI segmentation and classification using deep learning and machine learning approaches
The promising results affirm the potential of DL and ML approaches to enhance medical image analysis and improve diagnostic accuracy in brain tumor classification, potentially revolutionizing clinical workflows.
Authors
- Mohammad Abiad (ORCID: https://orcid.org/0000-0003-4641-4412)
- Xinde Li (ORCID: https://orcid.org/0000-0002-1529-4537)
- Faten Khalid Karim (ORCID: https://orcid.org/0000-0003-1111-5818)
- Samih M. Mostafa (ORCID: https://orcid.org/0000-0001-9234-5898)
- Aqib Ali (ORCID: https://orcid.org/0000-0001-9374-791X)
- Wali Khan Mashwani (ORCID: https://orcid.org/0000-0002-5081-741X)
Institutions
- Princess Nourah bint Abdulrahman University (SA)
- Kohat University of Science and Technology (PK)
- Shenzhen University (CN)
- South Valley University (EG)
- American University of the Middle East (KW)
- Southeast University (BD)
- Southeast University (CN)
Publication Details
- Journal
- Cancer Imaging
- Published
- 2025-11-13
- DOI
- https://doi.org/10.1186/s40644-025-00953-2
- Citations
- 1
- Primary Topic
- Brain Tumor Detection and Classification
- Type
- article
- Field-Weighted Citation Impact
- 0.84
Funders
- Princess Nourah Bint Abdulrahman University