Explainable multi-modal deep learning for transparent cancer diagnosis: integrating radiology, clinical features, and decision visualization
These results demonstrate that explainable multi-modal learning can effectively combine accuracy, interpret-ability, and robustness, supporting the development of reliable AI-based decision-support systems for cancer diagnosis.
Authors
- Yashwant V. Dongre (ORCID: https://orcid.org/0000-0002-4312-8704)
- Amol A. Bhosle (ORCID: https://orcid.org/0000-0003-4548-7193)
- Prof. Dr. Laxmi Bewoor
- Sital Dash
- Bhavnish Walia
- Banani Mohapatra
- Swami Kailas Patil (ORCID: https://orcid.org/0000-0002-1046-9860)
- Shrikant Jadhav
Institutions
- Ithaka Harbors (US)
- Walmart (United States) (US)
- Amazon (Germany) (DE)
- Shri Vishwakarma Skill University (IN)
- MIT Art, Design and Technology University (IN)
- San Jose State University (US)
Publication Details
- Journal
- Frontiers in Artificial Intelligence
- Published
- 2026-02-23
- DOI
- https://doi.org/10.3389/frai.2026.1767612
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00