Federated Learning Architecture for 3D Breast Cancer Image Classification.

backgroudsBreast cancer remains a major global health challenge, with early diagnosis playing a crucial role in improving patient survival rates. Among the available diagnostic techniques, mammography is widely employed for early detection. However, its effectiveness is often constrained by the complexity of image interpretation, which makes automated detection methods increasingly vital.

methodsIn this study, we propose an advanced approach that leverages 3D mammographic imaging and integrates Federated Learning (FL) to enable decentralized, privacy-preserving model training across multiple institutions. To evaluate the effectiveness of this approach, we assess various machine learning models, including Convolutional Neural Networks (CNNs), Transfer Learning architectures (VGG16, VGG19, ResNet50), and AutoEncoders (AEs), using 3D mammographic data.

resultsOur results indicate that the CNN model achieves an accuracy of 97.30%, which improves slightly to 97.37% when the model is combined with Federated Learning, highlighting both the predictive performance and privacy-preserving advantages of our method. In contrast, Transfer Learning models and AutoEncoders exhibit lower accuracies that range from 48.83% to 89.24%, revealing their limitations in the context of this specific task.

conclusionsThese findings underscore the effectiveness of the CNN-FL framework as a robust tool for breast cancer detection, showing that this approach offers a promising balance between diagnostic accuracy and data security-two critical factors in medical imaging.