A Systematic Literature Review on Classification of Brain Tumor Detection

Abstract

A tumor is a bloating or irregular growth caused by uncontrolled and unorganized cell division. Brain tumors are a hazardous type of tumor. Tumors in the brain are categorized into a few grades based on their severity. The grade, type, and position of the tumor determines the procedure of medical treatment for brain tumors. Tumors could be life-threatening if not discovered and properly treated at the initial stage. Professionals and doctors use magnetic resonance imaging images to detect brain tumors. Correctness accuracy is dependent on these experts' perceptions and specialized knowledge, and it is also tedious and expensive procedure. Multiple deep learning algorithms were proposed to identify the presence of tumors. However, these techniques have their own limitations and drawbacks. This work offers a thorough understanding of brain tumor detection, focusing primarily on its segmentation and classification by comparing and summarizing the most recent study work in this field. To the best of our knowledge, this is the only comprehensive study on classification of brain tumor detection using deep learning, machine learning and artificial intelligence models in recent literature.