Diagnosis of Hepatocellular Carcinoma - HCC Liver Cancer Using Federated Learning on MR Images

In recent years, Federated Learning has emerged as a powerful paradigm for training machine learning models across decentralized data sources while preserving data privacy. This study proposes a Federated Learning framework for the classification of liver tumors using Magnetic Resonance Imaging obtained from the ATLAS dataset, which provides contrast-enhanced images of hepatocellular carcinoma cases. A comparative evaluation was performed utilizing Convolutional Neural Network, EfficientNet, MobileNetV3, ResNet50, and VGG16 architectures within the federated environment. Among these models, the Federated Learning implementation based on EfficientNet achieved superior performance, reaching an accuracy of 93.75% and a ROC-AUC score of 99.19%. The results demonstrate that federated approaches can attain performance levels comparable to centralized learning while ensuring patient data confidentiality. This study highlights the applicability of Federated Learning in sensitive medical imaging tasks and emphasizes its potential for privacy-preserving collaborative model development. Future work may explore real-world deployment and scalability across heterogeneous clinical settings.