Leveraging Pre-trained Models for Robust Federated Learning for Kidney Stone Type Recognition

TL;DR

This paper proposes a federated learning framework enhanced with pre-trained models to improve kidney stone diagnosis accuracy and robustness against data corruption, addressing privacy and performance challenges in medical imaging.

Contribution

It introduces a novel two-stage FL framework combining pre-trained models with robustness validation for improved medical diagnosis accuracy.

Findings

Achieved 84.1% accuracy during learning parameter optimization

Attained 77.2% accuracy during robustness validation

Demonstrated enhanced robustness against image corruption

Abstract

Deep learning developments have improved medical imaging diagnoses dramatically, increasing accuracy in several domains. Nonetheless, obstacles continue to exist because of the requirement for huge datasets and legal limitations on data exchange. A solution is provided by Federated Learning (FL), which permits decentralized model training while maintaining data privacy. However, FL models are susceptible to data corruption, which may result in performance degradation. Using pre-trained models, this research suggests a strong FL framework to improve kidney stone diagnosis. Two different kidney stone datasets, each with six different categories of images, are used in our experimental setting. Our method involves two stages: Learning Parameter Optimization (LPO) and Federated Robustness Validation (FRV). We achieved a peak accuracy of 84.1% with seven epochs and 10 rounds during LPO stage, and 77.2% during FRV stage, showing enhanced diagnostic accuracy and robustness against image corruption. This highlights the potential of merging pre-trained models with FL to address privacy and performance concerns in medical diagnostics, and guarantees improved patient care and enhanced trust in FL-based medical systems.