Citation

Rahman, Sowad and Farid, Fahmid Al and Zabin, Mahe and Uddin, Jia and Abdul Karim, Hezerul (2025) Ultra-lightweight uncertainty-aware ensemble for large-scale multi-class medical MRI diagnosis. Frontiers in Radiology, 5. ISSN 2673-8740

Text fradi-5-1723272.pdf - Published Version Restricted to Repository staff only Download (2MB)

Abstract

This paper introduces an Ultra-Lightweight Uncertainty-Aware Ensemble (UALE) model for large-scale multi-class medical MRI diagnosis, evaluated on the 2024 Benchmark Diagnostic MRI and Medical Imaging Dataset containing 40 classes and 33,616 images. The model integrates five specialized micro- expert networks, each designed to capture distinct MRI features, and combines them using a confidence-weighted ensemble mechanism enhanced with variance-based uncertainty quantification for robust, reliable predictions. With only 0.05M parameters and 0.18 GFLOPs, UALE achieves high efficiency and competitive performance among ultra-lightweight models with an accuracy of 69.1% and an F1 score of 68.3%. Besides lightweight models, the paper offers an extensive analysis and performance comparison with fifteen state-of-the-art models, discusses various datasets, elaborates on uncertainty estimates pertaining to the clinical trustworthiness of the models and possible clinical deployment, and highlights trade-offs and avenues for future work in economically constrained settings. The extreme compactness and reliability of the UALE affords it unique utility in scalable medical diagnostics suitable for low-resource clinical settings and portable imaging devices, such as rural hospitals.

Item Type:	Article
Uncontrolled Keywords:	medical imaging, lightweight deep learning, ensemble, uncertainty quantification, MRI, multi-class classification, benchmark dataset
Subjects:	R Medicine > R Medicine (General) > R855-855.5 Medical technology
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	09 Feb 2026 02:14
Last Modified:	09 Feb 2026 02:14
URII:	http://shdl.mmu.edu.my/id/eprint/15225

Downloads

Edit (login required)