Decentralized and Explainable Brain Tumor Segmentation: A Blockchain-Secured Federated Learning Approach

Abstract

Brain tumor segmentation plays an important role in clinical diagnosis, treatment planning, and neuro-oncology assessment using multimodal Magnetic Resonance Imaging (MRI) data. However, conventional centralized deep learning systems often face limitations associated with patient data privacy, secure inter-institutional collaboration, and limited model interpretability. This study presents a decentralized and privacy-preserving brain tumor segmentation framework that integrates Federated Learning (FL), a blockchain-inspired audit and coordination mechanism, and Explainable Artificial Intelligence (XAI) within a collaborative medical imaging environment. A 3D U-Net architecture was trained on the BraTS 2020 dataset under a simulated multi-institutional federated setting using non-IID MRI data distributions. Federated Averaging (FedAvg) was employed for global model aggregation, while a blockchain-inspired permissioned ledger mechanism was used to record model update hashes and aggregation metadata for auditability across communication rounds. Grad CAM and LIME were incorporated to provide interpretable visualization of tumor related regions contributing to segmentation predictions. Experimental evaluation demonstrated stable convergence behavior with an overall voxel accuracy of 98.52%, a weighted F1 score of 98.3%, and Dice coefficient improvement from 0.752 to 0.830 during federated training. The generated segmentation outputs showed strong agreement with manually annotated tumor regions while preserving decentralized data privacy. The proposed framework contributes a secure, interpretable, and collaborative segmentation pipeline for trustworthy brain tumor analysis across distributed healthcare environments.