A comprehensive review on brain tumor segmentation and classification of MRI images

Abstract

In the analysis of medical images, one of the challenging tasks is the recognition of brain tumours via medical resonance images (MRIs). The diagnosis process is still tedious due to its complexity and considerable variety in tissues of tumor perception. Therefore, the necessities of tumor identification techniques are improving nowadays for medical applications. In the past decades, different approaches in the segmentation of various precisions and complexity degree have been accomplished, which depends on the simplicity and the benchmark of the technique. An overview of this analysis is to give out the summary of the semi-automatic techniques for brain tumor segmentation and classification utilizing MRI. An enormous amount of MRI based image data is accomplished using deep learning approaches. There are several works, dealing on the conventional approaches for MRI-based segmentation of brain tumor. Alternatively, in this review, we revealed the latest trends in the methods of deep learning. Initially, we explain the several threads in MRI pre-processing, including registration of image, rectification of bias field, and non-brain tissue dismissal. And terminally, the present state evaluation of algorithm is offered and forecasting the growths to systematise the MRI-based brain tumor into a regular cyclic routine in the clinical field are focussed.