KDH-Net: Explainable Medical AI for Multiclass Kidney Disease Characterization from CT Images
The results demonstrate that KDH-Net provides a stable, reliable, and interpretable framework for kidney CT characterization. The proposed system is designed to support clinical decision-making by offering trustworthy predictions under realistic evaluation conditions, rather than replacing clinical expertise.
Authors
- M. K. Abdullahi
- Zaka Ur Rehman (ORCID: https://orcid.org/0000-0003-1341-6366)
- Md Serajun Nabi (ORCID: https://orcid.org/0009-0000-5729-7640)
- Hezerul Bin Abdul Karim
- Hasanul Bannah (ORCID: https://orcid.org/0009-0004-4108-9299)
- Arbab Sufyan Wadood (ORCID: https://orcid.org/0009-0004-7067-1712)
- Istiyak Amin Santo (ORCID: https://orcid.org/0009-0002-5176-8282)
- Golam Md Mohiuddin
- Shahaba Alam
- Su Waddy Tun
Institutions
- Multimedia University (MY)
- Hospital Sultanah Bahiyah (MY)
Publication Details
- Journal
- Journal of Clinical Medicine
- Published
- 2026-04-21
- DOI
- https://doi.org/10.3390/jcm15083165
- Primary Topic
- Renal cell carcinoma treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00