The utilisation of federated learning in the classification of magnetic resonance images

The early and accurate diagnosis of liver tumors plays a crucial role in improving patient outcomes and determining appropriate treatment strategies. In recent years, machine learning techniques, particularly deep learning, have shown significant potential in enhancing diagnostic accuracy through medical image analysis. However, challenges such as data privacy and security concerns pose significant barriers to training deep learning models using centralized medical datasets. Federated Learning (FL) has emerged as a promising solution, enabling collaborative model training across multiple institutions without the need for sharing sensitive patient data. This study presents a novel application of Federated Learning for liver tumor classification using Magnetic Resonance Imaging scans. The proposed model leverages the EfficientNetB0 architecture within the FL framework. The performance of the FL model is compared to conventional deep learning architectures, including CNN, EfficientNet, MobileNetV3, ResNet50, and VGG16. Experimental results demonstrate that the FL-based EfficientNetB0 model achieves superior performance, with an accuracy of 93.75%, precision of 99.71%, recall of 87.79%, F1-score of 93.37%, and ROC-AUC of 99.19%. These results highlight the potential of FL to provide high classification performance while preserving data privacy. In conclusion, this study underscores the growing role of FL in the healthcare sector, where privacy-preserving AI solutions are becoming increasingly critical. Future work can explore the integration of FL with more advanced architectures and larger, more diverse datasets to further enhance model generalizability and robustness.