Brain tumor types and grades classification based on statistical feature set using support vector machine

In this paper, Support Vector Machine (SVM) method has been employed to perform classification of brain tumor images into their types and grades. Mainly the focus is on four brain tumor types-Normal, Glioma, Meningioma, Metastasis and the four grades of Astrocytomas, which is a common type of Glioma. This work experimented the SVM classifier using different statistical feature set such as first order features, second order features and both together and the final analysis shows that for brain tumor types and grades classification, second order features alone achieve an accuracy of 85% and 78.26% respectively, whereas first order features alone achieve 65.517% and 62.31% respectively and when both are used together 84.48% and 68.1% accuracy is achieved respectively. The experiments are repeated for different SVM types, kernel types and gamma values of kernel type. Analysis on the misclassification is performed for each feature set using specificity and sensitivity measures. At the end of this work, we inferred that the SVM classification method is classifying the brain tumor types satisfactorily but comparatively lacks in tumor grade classification.