Enhancing Brain Tumor Segmentation Accuracy through Scalable Federated Learning with Advanced Data Privacy and Security Measures

Faizan Ullah, Muhammad Nadeem, Mohammad Abrar, Farhan Amin (), Abdu Salam and Salabat Khan ()
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Faizan Ullah: Department of Computer Science and Software Engineering, International Islamic University, Islamabad 44000, Pakistan
Muhammad Nadeem: Department of Computer Science and Software Engineering, International Islamic University, Islamabad 44000, Pakistan
Mohammad Abrar: Department of Computer Science, Bacha Khan University, Charsadda 24420, Pakistan
Farhan Amin: Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
Abdu Salam: Department of Computer Science, Abdul Wali Khan University, Mardan 23200, Pakistan
Salabat Khan: IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China

Mathematics, 2023, vol. 11, issue 19, 1-27

Abstract: Brain tumor segmentation in medical imaging is a critical task for diagnosis and treatment while preserving patient data privacy and security. Traditional centralized approaches often encounter obstacles in data sharing due to privacy regulations and security concerns, hindering the development of advanced AI-based medical imaging applications. To overcome these challenges, this study proposes the utilization of federated learning. The proposed framework enables collaborative learning by training the segmentation model on distributed data from multiple medical institutions without sharing raw data. Leveraging the U-Net-based model architecture, renowned for its exceptional performance in semantic segmentation tasks, this study emphasizes the scalability of the proposed approach for large-scale deployment in medical imaging applications. The experimental results showcase the remarkable effectiveness of federated learning, significantly improving specificity to 0.96 and the dice coefficient to 0.89 with the increase in clients from 50 to 100. Furthermore, the proposed approach outperforms existing convolutional neural network (CNN)- and recurrent neural network (RNN)-based methods, achieving higher accuracy, enhanced performance, and increased efficiency. The findings of this research contribute to advancing the field of medical image segmentation while upholding data privacy and security.

Keywords: brain tumor segmentation; computation cost; data leakage; data privacy; deep learning; federated learning; medical imaging; scalability; U-Net (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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