Privacy-preserving breast disease detection via federated GA-optimized ensembles learning

Breast cancer is still one of the leading causes of death in women, and finding it early is essential for treatment to work. This study presents a sizeable deep learning architecture for classifying and segmenting breast ultrasound images. It utilizes federated learning to maintain patients' data privacy. The proposed pipeline begins with a thorough preprocessing stage that includes scaling, normalizing, and advanced image augmentation using affine and contrast-based changes. We employ four convolutional neural network architectures for hierarchical classification: ResNet50, EfficientNet, VGG16, and Xception. First, we distinguish between typical cases and abnormal ones. We then further classify abnormal images into benign and malignant classes. We employ an ensemble technique that combines the outputs of ResNet50 and EfficientNet through a weighted average optimized by a genetic algorithm to enhance the model's resilience. This method dramatically improves the classification's effectiveness, achieving higher accuracy and reliability. We use a federated learning system with the federated averaging (FedAvg) algorithm to improve data privacy. Our federated architecture maintains high accuracy while ensuring that the raw data stays at its local source. We test it with both single-client and multi-client setups. Ultimately, we employ a hybrid architecture that combines the feature maps of ResNet50 and EfficientNet to segment images of lesions known to be malignant. This yields significant spatial agreement with expert annotations. The Dice score and intersection over union (IoU) are two evaluation criteria that demonstrate the effectiveness of our segmentation model. This all-in-one system offers accurate and privacy-conscious breast ultrasound analysis, indicating that it could be beneficial in decentralized healthcare settings. The suggested method was tested using a standard breast magnetic resonance imaging (MRI) dataset, demonstrating its robustness and applicability in various situations. We used the accuracy, precision, recall, and F1-score to measure performance, and we found that the classification was accurate up to 96%. This paper discusses a scalable system that maintains people's privacy by utilizing ensemble learning, optimization-driven feature selection, and federated learning to aid doctors in early breast cancer detection using MRI data. This will make it easier for doctors to use this system in a broader range of medical tests.