Lightweight CNN for accurate brain tumor detection from MRI with limited training data
The results demonstrate that accurate brain tumor detection can be achieved with limited data using a carefully optimized CNN. Future work will expand datasets and integrate explainable AI for enhanced clinical integration.
Authors
- Shtwai Alsubai (ORCID: https://orcid.org/0000-0002-6584-7400)
- Abdelhamid Zaïdi (ORCID: https://orcid.org/0000-0003-1305-4959)
- Onur Osman (ORCID: https://orcid.org/0000-0001-7675-7999)
- Awad Bin Naeem (ORCID: https://orcid.org/0000-0002-1634-7653)
- Taner Çevik (ORCID: https://orcid.org/0000-0001-9653-5832)
- Jawad Rasheed (ORCID: https://orcid.org/0000-0003-3761-1641)
Institutions
- İstanbul Sabahattin Zaim Üniversitesi (TR)
- Prince Sattam Bin Abdulaziz University (SA)
- Applied Science Private University (JO)
- Qassim University (SA)
- İstanbul Nişantaşı Üniversitesi (TR)
- Istanbul Medipol University (TR)
- National College of Business Administration and Economics (PK)
- Istanbul Commerce University (TR)
- Istanbul University (TR)
- University of Bologna (IT)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-08-29
- DOI
- https://doi.org/10.3389/fmed.2025.1636059
- Citations
- 2
- Primary Topic
- Brain Tumor Detection and Classification
- Type
- article
- Field-Weighted Citation Impact
- 1.68