Review of automated computerized methods for brain tumor segmentation and classification

Recently, medical imaging and machine learning gained significant attention in the early detection of brain tumor. Compound structure and tumor variations, such as change of size, make brain tumor segmentation and classification a challenging task. In this review, we survey existing work on brain tumor, their stages, survival rate of patients after each stage, and computerized diagnosis methods. We discuss existing image processing techniques with a special focus on prepro-cessing techniques and their importance for tumor enhancement, tumor segmentation, feature extraction and features reduction techniques. We also provide the corresponding mathematical model-ing, classification, performance matrices, and finally important datasets. Last but not least, a de-tailed analysis of existing techniques is provided which is followed by future directions in this do-main.