A Comprehensive Analysis of Federated-Learning- Based Methodologies for Brain Tumor Diagnosis

Brain tumors have become a severe medical complication in recent years due to their high fatality rate. Radiologists detect the tumor manually, which is time-consuming, errorprone, and expensive. In recent years, automated detection based on deep learning has demonstrated promising results in solving computer vision problems such as image classification and segmentation. Many researchers have worked on various machine and deep learning approaches to determine the most optimal solution using the convolutional methodology. In this review paper, we discuss the most effective segmentation techniques based on the datasets that are widely used and publicly available. This study provides an overview of recent research on the diagnosis of brain tumors using federated and deep learning methods. We also proposed a survey of federated learning methodologies to enhance global segmentation performance and ensure privacy. A comprehensive literature review is suggested after studying more than 100 papers to generalize the most recent techniques in segmentation and multimodality information. Based on this review, future researchers will understand the powerful learning ability of deep learning mechanisms has been reviewed for their performance, and a comparison between them is presented to encourage its applications.