Efficient attention-based Ghost-ResNet for brain tumor classification in magnetic resonance imaging (MRI)
The results highlight the potential of pursuing efficiency-driven architectural designs as an alternative to increasingly complex deep learning models. In particular, channel-attention-assisted feature generation appears to preserve high diagnostic accuracy while reducing representational and computational overhead, supporting its suitability for resource-constrained medical imaging applications.
Authors
- Rabia Emhamed Al Mamlook (ORCID: https://orcid.org/0000-0002-2523-7819)
- Nahlah M. Shatnawi (ORCID: https://orcid.org/0000-0003-4117-5707)
- Mohammad Amin
- Ali Saeed Almuflih (ORCID: https://orcid.org/0000-0001-5359-1519)
- Abdullah Mohammed Al Fatais
- Salem Alhatamleh
- Amal Alishwait
- Khalid M. O. Nahar
Institutions
- Trine University (US)
- Al Yarmouk University College (IQ)
- Yarmouk Private University (SY)
- Artificial Intelligence in Medicine (Canada) (CA)
- King Khalid University (SA)
- Yarmouk University (JO)
Publication Details
- Journal
- Frontiers in Neuroscience
- Published
- 2026-02-23
- DOI
- https://doi.org/10.3389/fnins.2026.1778376
- Primary Topic
- Brain Tumor Detection and Classification
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- King Khalid University