Federated Transfer Learning for Lung Disease Detection

Abstract

Detecting lung disease traditionally relied on the expertise of doctors and medical practitioners. However, advancements in Artificial Intelligence have revolutionized this process by utilizing machine learning and deep learning algorithms to analyze X-ray and CT scan data. Despite the potential of these technologies, the use of private patient data for training models poses significant privacy concerns, as hospitals are reluctant to share such sensitive information. To address this issue, this paper presents a decentralized approach using Federated Learning, which secures patient data while overcoming the limitations of centralized data collection and storage. We propose a Federated Transfer Learning system that allows for effective model training without centralizing sensitive data. This approach leverages the decentralized nature of federated learning and the efficiency of transfer learning, enabling us to train models with limited data from each hospital while minimizing computing costs. We evaluated four methodologies—centralized, federated, transfer learning, and federated transfer learning—to determine their effectiveness in classifying lung diseases. Our findings demonstrate that Federated Transfer Learning is the most effective method, as it preserves user privacy by training models directly on client devices and achieves high accuracy. Specifically, the ResNet-50 model yielded the highest performance, with accuracies of 87.95%, 88.04%, 87.55%, and 89.96% for the centralized, transfer, federated, and federated transfer learning approaches, respectively. This study underscores the potential of Federated Transfer Learning to enhance both the accuracy of disease classification and the protection of patient privacy in medical applications.