A Compact Review on State-of-the-Art Brain Tumor Classification Models

Abstract

The brain tumor is normally caused by the occurrence of abnormal cells in the brain region. Malignant or cancerous and benign tumors are the two major types in the brain tumor classification. Brain tumor classification is the process of differentiating various stages of tumors like grading of gliomas as well as primary gliomas from metastases. The diagnosis of a brain tumor was made by the study of MR images. Some of the notable brain tumor classification techniques are knowledge-based techniques, support vector machine classifiers (SVM), and neural network classifiers. This survey intends to provide a review of65 paperson the topic of brain tumor classification. Mainly, the review comes with two major aspects: the analysis of classification algorithms and the analysis of segmentation algorithms. At first, aclear literature review is made in terms of various brain tumor classification models. Subsequently, the analysis is made under the performance measure especially the accuracy rate is analyzed from all the reviewed papers. Further analysis is made regarding the used dataset, image modalities, and the used optimization concept as well. All the analytical results are explained in terms of tabulation and diagrammatic graphical representation. Finally, the clear problem statement is described showingthe different challenges faced in the classification process and the future direction that is to be made.