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Brain Imaging

Brain Imaging

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Updated: Sep 17, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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Published on: April 13, 2013

A federated learning-based privacy-preserving image processing framework for brain tumor detection from CT scans.

Abdullah Al-Saleh¹, Ghanshyam G Tejani^2,3, Shailendra Mishra⁴

¹Department of Computer Engineering, College of Computer and Information Sciences, Majmaah University, Majmaah, 11952, Saudi Arabia.

Scientific Reports

|July 2, 2025

Summary

This study introduces a novel Aniso-ResCapHGBO-Net framework for privacy-preserving brain tumor detection using decentralized AI. The model achieves high accuracy, enhancing early diagnosis while protecting patient data.

Keywords:

Blockchain security in healthcare Brain tumor detection Capsule networks Deep learning in medical imaging Federated learning HGBOA Privacy-preserving machine learning ResNet-50 Secure collaborative learning

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Area of Science:

Medical Imaging
Artificial Intelligence
Data Privacy

Background:

Accurate brain tumor detection is vital for patient outcomes.
Traditional deep learning models pose privacy and regulatory challenges due to centralized data storage.
Decentralized systems are needed to address data heterogeneity and privacy concerns in healthcare.

Purpose of the Study:

To develop a privacy-preserving, decentralized framework for brain tumor detection.
To enhance feature extraction and preserve spatial information in medical images.
To ensure secure and tamper-evident model updates on a private blockchain.

Main Methods:

Anisotropic-residual capsule hybrid Gorilla Badger optimized network (Aniso-ResCapHGBO-Net) framework.
Integration of ResNet-50 and capsule networks for advanced feature extraction.
Hybrid Gorilla Badger optimization algorithm (HGBOA) for feature selection.
Preprocessing: anisotropic diffusion filtering, morphological operations, mutual information-based image registration.
Secure updates via Ethereum private blockchain and SHA-256 hashing.

Main Results:

Achieved 99.07% accuracy, 98.54% precision, and 99.82% sensitivity on benchmark CT brain tumor datasets.
Demonstrated reduction in false negatives and false positives.
Validated the framework's effectiveness in a decentralized, privacy-preserving manner.

Conclusions:

The Aniso-ResCapHGBO-Net framework offers a robust solution for accurate brain tumor detection.
The decentralized and privacy-preserving approach addresses critical healthcare data challenges.
This method successfully balances high diagnostic performance with stringent data protection requirements.