Federated Lung Cancer Prediction Using Histopathological Medical Images

Machine learning is increasingly significant in health science because it can infer valuable information from high-dimensional data. However, combining research and patient data from various organizations and hospitals is frequently not practical due to privacy concerns. In this research, we conduct a study of federated learning for lung cancer prediction to demonstrate the effectiveness of collaborative and decentralized learning in a context where data is privacy preserved. We also conducted visual interpretation using GradCAM to validate the robust performance of the decentralized method in predicting lung cancer.