The diagnosis of tumors in the initial stage plays a crucial role in improving the clinical
outcomes of a patient. Evaluation of brain tumors from many MRI images generated regularly in a
clinical environment is a complex and time-consuming process. Therefore,there comes a need for an
efficient and accurate model for the early detection of tumors. This paper revolves around the current
strategies used for brain tumor segmentation and classification from MRI images of the brain. This
approach also tries to pave the way for the significance of their performance measure and quantitative
evaluation of forefront strategies. This state of the art clearly describes the importance of several brain
image segmentation and classification methodsduring the past 13 years of publication by various researchers.
In this instance, new calculations are being made for potential clients to analyze the concerned
area of research. This review acknowledges the key accomplishments expressed in the diagnostic
measures and their success indicators of qualitative and quantitative measurement. This research
study also explores the key outcomes and reasons for finding the lessons learned.