TY  - JOUR
AU  - Mukhtar, Aiza
AU  - Ali, Aamir
AU  - Farooq, Wajiha
AU  - Fareed, Muhammad Aqib
AU  - Sohail, Sadia
AU  - Ahsan, Azka
PY  - 2026
DA  - 2026/08/17
TI  - Decentralized and Explainable Brain Tumor Segmentation: A Blockchain-Secured Federated Learning Approach
JO  - ICCK Journal of Image Analysis and Processing
T2  - ICCK Journal of Image Analysis and Processing
JF  - ICCK Journal of Image Analysis and Processing
VL  - 2
IS  - 3
SP  - 168
EP  - 189
DO  - 10.62762/JIAP.2026.354772
UR  - https://www.icck.org/article/abs/JIAP.2026.354772
KW  - brain tumor segmentation
KW  - federated learning
KW  - blockchain-inspired audit logging
KW  - explainable AI
KW  - privacy preserving AI
KW  - medical diagnostics
AB  - 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.
SN  - 3068-6679
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Mukhtar2026Decentrali,
  author = {Aiza Mukhtar and Aamir Ali and Wajiha Farooq and Muhammad Aqib Fareed and Sadia Sohail and Azka Ahsan},
  title = {Decentralized and Explainable Brain Tumor Segmentation: A Blockchain-Secured Federated Learning Approach},
  journal = {ICCK Journal of Image Analysis and Processing},
  year = {2026},
  volume = {2},
  number = {3},
  pages = {168-189},
  doi = {10.62762/JIAP.2026.354772},
  url = {https://www.icck.org/article/abs/JIAP.2026.354772},
  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.},
  keywords = {brain tumor segmentation, federated learning, blockchain-inspired audit logging, explainable AI, privacy preserving AI, medical diagnostics},
  issn = {3068-6679},
  publisher = {Institute of Central Computation and Knowledge}
}