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

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.

Employing deep learning and transfer learning for accurate brain tumor detection

“…The method surpasses existing CNN and RNN-based approaches, achieving higher accuracy, performance, and efficiency. The findings hold promise for wider adoption in medical imaging applications without compromising data confidentiality 31 . A hybrid methodology for brain tumor segmentation in MRI scans, combining handcrafted features with convolutional neural networks.…”

ContourTL-Net: Contour-Based Transfer Learning Algorithm for Early-Stage Brain Tumor Detection

“…The primary goal of medical image processing is to clean MRI images and reduce artifacts to obtain a better feature of the represented image by using a variety of image processing techniques such as brightness correction, contrast enhancement, noise reduction, morphological operation, and unnecessary object removal. To complete this research, a region-based segmentation technique is utilized due to its computational efficiency and simplicity nature [ 32 ]. The segmentation strategy has been enhanced by extracting the contour area from the image.…”

“…A contour is defined as a simple curve that connects all straight points (along the boundary) that have the same color or intensity. Tumor segmentation might be affected due to the sensitivity to the selection of seed point preference [ 32 ]. Initializing seed points in the form of contour guides the effective segmentation process [ 34 ].…”