Distributed Federated Learning-Based Deep Learning Model for Privacy MRI Brain Tumor Detection

arXiv:2404.10026v1 Announce Type: cross
Abstract: Distributed training can facilitate the processing of large medical image datasets, and improve the accuracy and efficiency of disease diagnosis while protecting patient privacy, which is crucial for achieving efficient medical image analysis and accelerating medical research progress. This paper presents an innovative approach to medical image classification, leveraging Federated Learning (FL) to address the dual challenges of data privacy and efficient disease diagnosis. Traditional Centralized Machine Learning models, despite their widespread use in medical …

abstract accuracy analysis arxiv brain cs.cr cs.lg datasets deep learning detection diagnosis disease disease diagnosis distributed eess.iv efficiency federated learning image image datasets medical medical research mri paper patient privacy processing progress research training type