A Literature Review on Brain Tumor Detection and Segmentation
An extensive and exhaustive guide to the sub-field of Brain Tumor Detection, focusing primarily on its segmentation and classification, has been presented by comparing and summarizing the latest research work done in this domain.
Abstract
A tumor is a swelling or abnormal growth resulting from the division of cells in an uncontrolled and disorderly manner. Brain tumors are an exceptionally threatening kind of tumor. There exist several types of brain tumors which are classified into four grades. The process for the medical treatment of brain tumors depends on the type, the grade as well as the location of the tumor. If not detected at the early stages, brain tumors can turn out to be fatal. Magnetic Resonance Imaging (MRI) images are used by specialists and neurosurgeons for the diagnosis of brain tumors. The accuracy depends on the experience and domain knowledge of these experts, and is also a time consuming and expensive process. To overcome these restrictions, several deep learning algorithms have been proposed for the detection of presence of brain tumors. In this review paper, an extensive and exhaustive guide to the sub-field of Brain Tumor Detection, focusing primarily on its segmentation and classification, has been presented by comparing and summarizing the latest research work done in this domain. This research work has made a comparison between 28 research papers and highlighted the different state-of-the-art approaches. With a lot of ongoing research work in this area, this paper would assist all future researchers.