Brain tumor classification using GAN-augmented data with autoencoders and Swin Transformers
These findings indicate that the proposed approach effectively addresses data imbalance and feature extraction limitations, leading to superior performance in brain tumor classification. Future work will focus on real-time clinical deployment and expanding the model's application to various medical imaging tasks.
Authors
- Abdullah Almuhaimeed (ORCID: https://orcid.org/0000-0002-1155-9382)
- Anas Bilal (ORCID: https://orcid.org/0000-0002-7760-3374)
- Abdulkareem Alzahrani (ORCID: https://orcid.org/0000-0003-3658-1284)
- Malek Alrashidi (ORCID: https://orcid.org/0000-0001-8460-9240)
- Mansoor Alghamdi (ORCID: https://orcid.org/0000-0002-2891-6374)
- Raheem Sarwar (ORCID: https://orcid.org/0000-0002-0640-807X)
Institutions
- Manchester Metropolitan University (GB)
- King Abdulaziz City for Science and Technology (SA)
- Hainan Normal University (CN)
- Al Baha University (SA)
- University of Tabuk (SA)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-08-22
- DOI
- https://doi.org/10.3389/fmed.2025.1635796
- Citations
- 5
- Primary Topic
- Brain Tumor Detection and Classification
- Type
- article
- Field-Weighted Citation Impact
- 4.21