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This study proposes a federated learning framework to enhance brain tumor segmentation accuracy while maintaining data privacy and security. By utilizing a U-Net-based model architecture, the approach improves segmentation performance significantly, achieving a specificity of 0.96 and a dice coefficient of 0.89 as the number of clients increases. The research highlights the potential of federated learning in medical imaging applications, addressing challenges associated with centralized data sharing and pri

Author

Tejesh Chowdary

Language

EN