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Federated Learning Architecture for 3D Breast Cancer Image Classification
Amel Ali Alhussan1, Wiem Nhidi2, Imen Filali1
1Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 84428, Saudi Arabia.
Cancers
|November 13, 2025
Summary
A novel Convolutional Neural Network (CNN) combined with Federated Learning (FL) significantly improves automated breast cancer detection using 3D mammography. This approach enhances diagnostic accuracy while preserving patient data privacy.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Oncology
Background:
- Breast cancer diagnosis relies heavily on mammography, but interpretation can be challenging.
- Automated detection methods are crucial for improving diagnostic accuracy and efficiency.
- Early detection significantly enhances patient survival rates for breast cancer.
Purpose of the Study:
- To develop and evaluate an advanced automated breast cancer detection system.
- To integrate 3D mammographic imaging with Federated Learning (FL) for privacy-preserving, decentralized model training.
- To compare the performance of Convolutional Neural Networks (CNNs), Transfer Learning models, and AutoEncoders for this task.
Main Methods:
- Utilized 3D mammographic imaging data for model training and evaluation.
- Implemented and compared various machine learning models: CNNs, Transfer Learning (VGG16, VGG19, ResNet50), and AutoEncoders (AEs).
- Employed Federated Learning (FL) to enable decentralized and privacy-preserving model training across multiple institutions.
Main Results:
- The CNN model achieved a high accuracy of 97.30%.
- Combining the CNN with Federated Learning (CNN-FL) slightly improved accuracy to 97.37%, demonstrating robust predictive performance.
- Transfer Learning models and AutoEncoders showed lower accuracies, ranging from 48.83% to 89.24%.
Conclusions:
- The CNN-FL framework is a highly effective tool for automated breast cancer detection.
- This approach successfully balances high diagnostic accuracy with crucial data security.
- The findings highlight the potential of federated learning in enhancing medical imaging analysis while maintaining patient privacy.
