Abstract
The brain tumor is considered the deadly disease of the century. At present, neuroscience and artificial intelligence conspire in the timely delineation, detection, and classification of brain tumors. The process of manually classifying and segmenting many volumes of MRI scans is a challenging and laborious task. Therefore, there is an essential requirement to build computer-aided diagnosis systems to diagnose brain tumors timely. Herein review focuses on the advances of the last decade in brain tumor segmentation, feature extraction, and classification through powerful and versatile brain imaging modality Magnetic Resonance Imaging (MRI). However, particular emphasis on deep learning and hybrid techniques. We have summarized the work of researchers published in the last decade (2010–2019) termed as the 10s and the present decade (only including the year 2020) termed as the 20s. The decades in review reveal the bore witness to the critical revolutionary paradigm shift in artificial intelligence viz. conventional/machine learning methods, emerged deep learning, and emerging hybrid techniques. This review also covers some persistent concerns on using the type of classifier and striking trends in commonly employed MRI modalities for brain tumor diagnosis. Moreover, this study ensures the limitation, solutions, and future trends or opens up the researchers’ advanced challenges to develop an efficient system exhibiting clinically acceptable accuracy that assists the radiologists for the brain tumor prognosis.