A federated learning-based privacy-preserving image processing framework for brain tumor detection from CT scans.
Authors
Affiliations (6)
Affiliations (6)
- Department of Computer Engineering, College of Computer and Information Sciences, Majmaah University, Majmaah, 11952, Saudi Arabia.
- Department of Research Analytics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, 600077, India. [email protected].
- Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan, 320315, Taiwan. [email protected].
- Department of Computer Engineering, College of Computer and Information Sciences, Majmaah University, Majmaah, 11952, Saudi Arabia. [email protected].
- Department of Information System, College of Computer and Information Sciences, Majmaah University, Majmaah, 11952, Saudi Arabia.
- Department of Computer and Electrical Engineering, Mid Sweden University, Sundsvall, Sweden. [email protected].
Abstract
The detection of brain tumors is crucial in medical imaging, because accurate and early diagnosis can have a positive effect on patients. Because traditional deep learning models store all their data together, they raise questions about privacy, complying with regulations and the different types of data used by various institutions. We introduce the anisotropic-residual capsule hybrid Gorilla Badger optimized network (Aniso-ResCapHGBO-Net) framework for detecting brain tumors in a privacy-preserving, decentralized system used by many healthcare institutions. ResNet-50 and capsule networks are incorporated to achieve better feature extraction and maintain the structure of images' spatial data. To get the best results, the hybrid Gorilla Badger optimization algorithm (HGBOA) is applied for selecting the key features. Preprocessing techniques include anisotropic diffusion filtering, morphological operations, and mutual information-based image registration. Updates to the model are made secure and tamper-evident on the Ethereum network with its private blockchain and SHA-256 hashing scheme. The project is built using Python, TensorFlow and PyTorch. The model displays 99.07% accuracy, 98.54% precision and 99.82% sensitivity on assessments from benchmark CT imaging of brain tumors. This approach also helps to reduce the number of cases where no disease is found when there is one and vice versa. The framework ensures that patients' data is protected and does not decrease the accuracy of brain tumor detection.