Systematic survey on generative adversarial networks for brain tumor segmentation and classification

Abstract

SummaryBrain cancer is one of the leading diseases of death in the world caused due to unwanted proliferation of cells. If left untreated, this growth can spread into other human body parts. So early detection helps to enhance long‐term survival and reduce the mortality rate. A lot of research has been done in the field of brain tumors. There have been numerous previous studies conducted using traditional approaches. So far, few review studies are available on brain tumor segmentation and classification and moreover, they were quite limited in explaining the connections of Generative Adversarial Networks (GAN) for brain tumor segmentation and classification. This review mainly focuses on the GAN architectures for brain tumor segmentation and classification. Therefore, first, an overview of brain tumors, GAN‐based publications, approaches to brain tumor segmentation and classification, data sets, software platforms, and performance parameters, are presented. Finally, GAN applications and current trends are discussed. This thorough survey will aid researchers and beginners in the future in creating a better decision support system.