A scalable and reliable deep learning framework for enhanced brain tumor detection and diagnosis using AI-based medical imaging
It therefore proposes a two-layer deep learning architecture that effectively incorporates precise tumor localization into explicit binary tumor detection. Beyond this, the work focuses on practical clinical applicability, robust data governance, and deployment-ready systems rather than diagnosing subtypes of tumors.
Authors
- Eali Stephen Neal Joshua (ORCID: https://orcid.org/0000-0002-3400-1242)
- Humaira Khanam
- Thirupathi Rao Nakka
- Naceur Chihaoui
- Tahani Alsubait
- Sultan Ahmad
Institutions
- Lovely Professional University (IN)
- Prince Sattam Bin Abdulaziz University (SA)
- Vignan's Foundation for Science, Technology & Research (IN)
- Umm al-Qura University (SA)
- GIET University (IN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2026-01-30
- DOI
- https://doi.org/10.3389/fmed.2026.1738796
- Primary Topic
- Brain Tumor Detection and Classification
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Prince Sattam bin Abdulaziz University