XAI-BT-EdgeNet: explainable edge-aware deep learning with squeeze-and-excitation for brain tumor detection and prediction
This work includes the development of a dual-branch CNN architecture that combines semantic and edge features for enhanced classification, the integration of SE modules to highlight clinically significant regions, and the application of multi-method XAI to offer transparent, interpretable outputs for clinical applicability. Overall, XAI-BT-EdgeNet delivers a high-performing, interpretable solution that bridges the gap between deep learning and trustworthy clinical decision-making in brain tumor diagnosis.
Authors
- Shrikant Tiwari (ORCID: https://orcid.org/0000-0001-6947-2362)
- Tarun Agarwal (ORCID: https://orcid.org/0009-0004-5682-8234)
- Deependra Rastogi (ORCID: https://orcid.org/0000-0002-0537-3047)
- Prashant Johri (ORCID: https://orcid.org/0000-0001-8771-5700)
- Pushpa Singh (ORCID: https://orcid.org/0000-0001-9796-3978)
- Massimo Donelli (ORCID: https://orcid.org/0000-0001-9477-9243)
Institutions
- Jaypee Institute of Information Technology (IN)
- Galgotias University (IN)
- University of Trento (IT)
- IILM Institute for Higher Education (IN)
Publication Details
- Journal
- Frontiers in Artificial Intelligence
- Published
- 2025-11-20
- DOI
- https://doi.org/10.3389/frai.2025.1676524
- Citations
- 1
- Primary Topic
- Brain Tumor Detection and Classification
- Type
- article
- Field-Weighted Citation Impact
- 0.84