Harnessing ResUHybridNet with Federated Learning: A New Paradigm in Brain Tumour Segmentation

Precise segmentation of brain tumors from MRI scans remains a formidable challenge, driven by the critical demand for accuracy in medical imaging.To surmount this challenge, our paper introduces the Federated ResUHybridNet-a cutting-edge methodology that harmonizes the resilience of ResNet with the precision of U-Net.Nestled within the sophisticated realm of federated learning, this innovative architecture fosters collaborative model training, optimizing the training process and steadfastly upholding stringent data privacy standards.The methodology employs a Federated learning framework for collaborative model training across multiple hospital nodes.It features the ResUHybridNet architecture, combining the deep feature learning of ResNet with the detailed segmentation capabilities of U-Net.This integration optimizes brain tumor segmentation by synergizing the strengths of both architectures.Furthermore, the decentralized ResUHybridNet model undergoes fine-tuning by leveraging the local data of each individual participating hospital.The study is dedicated to the segmentation of brain tumors using 3D MRI scans as the imaging modality.The dataset employed encompasses 3D volumetric data, enhancing the depth and spatial understanding crucial for the evaluation of the Federated ResUHybridNet architecture.Overall, the Federated ResUHybridNet model significantly advances data privacy measures through federated learning and achieves optimized model performance.These contributions mark a notable stride towards enhancing brain tumor diagnosis standards and refining subsequent treatment strategies.