Paper Title
A Review on Brain Tumour Detection and Classification

A tumour is a swelling or abnormal growth resulting from the division of cells in an uncontrolled and disorderly manner. Brain tumour are an exceptionally threatening kind of tumour. There existseveral types of brain tumour which are classified into four grades. The process for the medical treatment of brain tumour depends on the type, the grade as well as the location of the tumour. If not detected at the early stages, brain tumour can turn out to be fatal. Magnetic Resonance Imaging (MRI) images are used by specialists and neurosurgeons for the diagnosis of brain tumour. 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 tumour. In this review paper, an extensive and exhaustive guide to the sub-field of Brain Tumour 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 compare is on 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 ass is tall future researchers. Keywords – Brain Tumour Detection, Deep Learning, Image Segmentation, MRI images, Medical Imaging, Tumour Segmentation