부산대학교 도서관

Breast cancer stands as one of the predominant health challenges globally, affecting millions of women every year and necessitating early and accurate detection to optimize patient outcomes. Currently, while deep convolutional neural networks (DCNNs) have shown promise in breast cancer detection, their application is often hampered by privacy concerns associated with sharing patient data and the limitation of training on small, localized datasets. Addressing these challenges, this manuscript introduces an effective federated learning approach tailored for breast cancer detection, leveraging DCNNs across diverse and large datasets without compromising data privacy. Our experimental findings underscore significant advancements in detection accuracy of 98.9% on three large scale datasets which are VINDR-MAMMO, CMMD, and INBREAST. Additionally, we tested the proposed federated learning performance, showcasing the potential of our approach as a robust and privacy-preserving solution for future breast cancer diagnostic strategies.