CNN-TumorNet: leveraging explainability in deep learning for precise brain tumor diagnosis on MRI images
Despite the efficacy of CNN-TumorNet, the overarching challenge of deep learning interpretability persists. These models may function as "black boxes," complicating doctors' ability to trust and accept them without comprehending their rationale. By integrating LIME, CNN-TumorNet achieves elevated accuracy alongside enhanced transparency, facilitating its application in clinical environments and improving patient care in neuro-oncology.
Authors
- Niyaz Ahmad Wani (ORCID: https://orcid.org/0000-0002-7656-3374)
- Javaid Iqbal Bhat (ORCID: https://orcid.org/0000-0003-0312-4888)
- Miltiadis D. Lytras (ORCID: https://orcid.org/0000-0002-7281-5458)
- Sandeep Saharan (ORCID: https://orcid.org/0000-0002-2152-6875)
- V.K. Sharma (ORCID: https://orcid.org/0000-0001-6876-4814)
- Hind Alsharif (ORCID: https://orcid.org/0000-0001-6942-3130)
- Bassma Saleh Alsulami
- Novsheena Rasool (ORCID: https://orcid.org/0000-0001-6405-6415)
Institutions
- King Abdulaziz University (SA)
- Umm al-Qura University (SA)
- The American College of Greece (GR)
- IILM Institute for Higher Education (IN)
- Bennett University (IN)
- Woxsen School of Business (IN)
- Islamic University of Science and Technology (IN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-03-26
- DOI
- https://doi.org/10.3389/fonc.2025.1554559
- Citations
- 33
- Primary Topic
- Explainable Artificial Intelligence (XAI)
- Type
- article
- Field-Weighted Citation Impact
- 81.87