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Empowering Healthcare through Privacy-Preserving MRI Analysis

Al Amin, Kamrul Hasan, Saleh Zein-Sabatto, Deo Chimba, Liang Hong, Imtiaz Ahmed, Tariqul Islam

TL;DR

The paper tackles privacy constraints in MRI data sharing for brain tumor classification and proposes Ensemble-Based Federated Learning (EBFL), which fuses feature-sharing across four clients with a maximum voting ensemble of CNNs to build a robust global predictor without exchanging raw data. By combining MobileNetV2, VGG16, and VGG19 in a federated setting, the approach achieves 94% precision for the global FL model and 96% for the ensemble, demonstrating improved accuracy under data-siloed, privacy-preserving conditions. This method offers a practical pathway to accurate, privacy-aware MRI diagnostics in healthcare, albeit with higher computational and communication demands for coordinating distributed learning.

Abstract

In the healthcare domain, Magnetic Resonance Imaging (MRI) assumes a pivotal role, as it employs Artificial Intelligence (AI) and Machine Learning (ML) methodologies to extract invaluable insights from imaging data. Nonetheless, the imperative need for patient privacy poses significant challenges when collecting data from diverse healthcare sources. Consequently, the Deep Learning (DL) communities occasionally face difficulties detecting rare features. In this research endeavor, we introduce the Ensemble-Based Federated Learning (EBFL) Framework, an innovative solution tailored to address this challenge. The EBFL framework deviates from the conventional approach by emphasizing model features over sharing sensitive patient data. This unique methodology fosters a collaborative and privacy-conscious environment for healthcare institutions, empowering them to harness the capabilities of a centralized server for model refinement while upholding the utmost data privacy standards.Conversely, a robust ensemble architecture boasts potent feature extraction capabilities, distinguishing itself from a single DL model. This quality makes it remarkably dependable for MRI analysis. By harnessing our groundbreaking EBFL methodology, we have achieved remarkable precision in the classification of brain tumors, including glioma, meningioma, pituitary, and non-tumor instances, attaining a precision rate of 94% for the Global model and an impressive 96% for the Ensemble model. Our models underwent rigorous evaluation using conventional performance metrics such as Accuracy, Precision, Recall, and F1 Score. Integrating DL within the Federated Learning (FL) framework has yielded a methodology that offers precise and dependable diagnostics for detecting brain tumors.

Empowering Healthcare through Privacy-Preserving MRI Analysis

TL;DR

The paper tackles privacy constraints in MRI data sharing for brain tumor classification and proposes Ensemble-Based Federated Learning (EBFL), which fuses feature-sharing across four clients with a maximum voting ensemble of CNNs to build a robust global predictor without exchanging raw data. By combining MobileNetV2, VGG16, and VGG19 in a federated setting, the approach achieves 94% precision for the global FL model and 96% for the ensemble, demonstrating improved accuracy under data-siloed, privacy-preserving conditions. This method offers a practical pathway to accurate, privacy-aware MRI diagnostics in healthcare, albeit with higher computational and communication demands for coordinating distributed learning.

Abstract

In the healthcare domain, Magnetic Resonance Imaging (MRI) assumes a pivotal role, as it employs Artificial Intelligence (AI) and Machine Learning (ML) methodologies to extract invaluable insights from imaging data. Nonetheless, the imperative need for patient privacy poses significant challenges when collecting data from diverse healthcare sources. Consequently, the Deep Learning (DL) communities occasionally face difficulties detecting rare features. In this research endeavor, we introduce the Ensemble-Based Federated Learning (EBFL) Framework, an innovative solution tailored to address this challenge. The EBFL framework deviates from the conventional approach by emphasizing model features over sharing sensitive patient data. This unique methodology fosters a collaborative and privacy-conscious environment for healthcare institutions, empowering them to harness the capabilities of a centralized server for model refinement while upholding the utmost data privacy standards.Conversely, a robust ensemble architecture boasts potent feature extraction capabilities, distinguishing itself from a single DL model. This quality makes it remarkably dependable for MRI analysis. By harnessing our groundbreaking EBFL methodology, we have achieved remarkable precision in the classification of brain tumors, including glioma, meningioma, pituitary, and non-tumor instances, attaining a precision rate of 94% for the Global model and an impressive 96% for the Ensemble model. Our models underwent rigorous evaluation using conventional performance metrics such as Accuracy, Precision, Recall, and F1 Score. Integrating DL within the Federated Learning (FL) framework has yielded a methodology that offers precise and dependable diagnostics for detecting brain tumors.
Paper Structure (11 sections, 7 equations, 4 figures, 1 table, 2 algorithms)

This paper contains 11 sections, 7 equations, 4 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. Methodology
  4. EBFL based MR images System Description
  5. Mathematical Formulation for the Proposed Maximum Voting Classifier Techniques incorporating federated learning
  6. Multi-Class Brain Tumor Classification Using Federated Learning mathematical explanation
  7. Experimental Analysis
  8. Dataset Description & experimental result
  9. Evaluating DL Models in Brain Tumor Classification: A Comparative Analysis
  10. Global model (FL) evaluation matrices analysis
  11. Conclusion

Figures (4)

  • Figure 1: Ensemble Based Federated Learning (EBFL) framework for analyzing MR Images
  • Figure 2: Ensemble & Base models confusion matrices for Brain tumor multi-class classification )
  • Figure 3: Ensemble & Base models Training and Validation loss and accuracy for identifying brain tumor
  • Figure 4: Federated Learning model confusion matrix for brain tumor classification at the local server )