Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019 - Tiwari, Arti; Srivastava, Shilpa; Pant, Millie (Pattern recognition letters, 2020)
•Detailed discussion on the significance of MRI for brain tumor detection and classification of brain tumors.•Focus on Deep learning and metaheuristics methods for brain tumor classification through image segmentation.•Analysis of recent deep learning and metaheuristics methods from 2014 to 2019. The past few years have witnessed a significant increase in medical cases related to brain tumors, making it the 10th most common form of tumor affecting children and adults alike. However, it is also one of the most curable forms of tumors if detected well on time. Consequently scientists and researchers have been working towards developing sophisticated techniques and methods for identifying the form and stage of tumor. Magnetic Resonance Imaging (MRI) and Computer Tomography (CT) are two methods widely used for resectioning and examining the abnormalities in terms of shape, size or location of brain tissues which in turn help in detecting the tumors. MRI, due to its advantages over CT scan, discussed later in the paper, is preferred more by the doctors. The way towards sectioning tumor from MRI picture of a brain cerebrum is one of the profoundly engaged regions in the network of medical science as MRI is non-invasive imaging. This paper provides a systematic literature survey of techniques for brain tumor segmentation and classification of abnormality and normality from MRI images based on different methods including deep learning techniques, metaheuristic techniques and hybridization of these two. It includes presentation and quantitative investigation used in conventional segmentation and classification techniques.