A Privacy-Enhanced Framework for Chest Disease Classification Using Federated Learning and Blockchain

Abstract

This study presents a novel approach for the early diagnosis of prevalent chest diseases, including COVID-19, pneumonia, and lung cancer, utilizing advanced machine learning techniques. The research focuses on addressing the limitations of traditional diagnostic methods by introducing Federated Learning as a collaborative and privacy-preserving solution. By leveraging Federated Learning, stakeholders can collectively develop accurate diagnostic models without directly sharing sensitive medical data, ensuring both privacy and diagnostic accuracy. Furthermore, the study proposes a multi-classification Federated Learning method enhanced by blockchain technology to reinforce data security and privacy. Experimental results demonstrate the effectiveness of this approach compared to centralized models, showcasing comparable performance in terms of accuracy and superior achievement in terms of privacy preservation. The integration of blockchain into the Federated Learning framework holds promise for a robust system prioritizing data privacy and security in the healthcare domain. This innovative combination not only advances machine learning in medical diagnostics but also sets a forward-looking approach for safeguarding patient information in today’s data-driven healthcare landscape.