A Transfer Learning Approach to Breast Cancer Classification in a Federated Learning Framework

Abstract

Artificial intelligence (AI) technologies have seen strong development. Many applications now use AI to diagnose breast cancer. However, most new research has only been conducted in centralized learning (CL) environments, which entails the risk of privacy breaches. Moreover, the accurate identification and localization of lesions and tumor prediction using AI technologies is expected to increase patients' likelihood of survival. To address these difficulties, we developed a federated learning (FL) facility that extracts features from participating environments rather than a CL facility. This study's novel contributions include (i) the application of transfer learning to extract data features from the region of interest (ROI) in an image, which aims to enable careful pre-processing and data enhancement for data training purposes; (ii) the use of synthetic minority oversampling technique (SMOTE) to process data, which aims to more uniformly classify data and improve diagnostic prediction performance for diseases; (iii) the application of FeAvg-CNN + MobileNet in an FL framework to ensure customer privacy and personal security; and (iv) the presentation of experimental results from different deep learning, transfer learning and FL models with balanced and imbalanced mammography datasets, which demonstrate that our solution leads to much higher classification performance than other approaches and is viable for use in AI healthcare applications.