An empirical study of different machine learning techniques for brain tumor classification and subsequent segmentation using hybrid texture feature

Brain tumor classification and segmentation for different weighted MRIs are among the most tedious tasks for many researchers due to the high variability of tumor tissues based on texture, structure, and position. Our study is divided into two stages: supervised machine learning-based tumor classification and image processing-based region of tumor extraction. For this job, seven methods have been used for texture feature generation. We have experimented with various state-of-the-art supervised machine learning classification algorithms such as support vector machines (SVMs), K-nearest neighbors (KNNs), binary decision trees (BDTs), random forest (RF), and ensemble methods. Then considering texture features into account, we have tried for fuzzy C-means (FCM), K-means, and hybrid image segmentation algorithms for our study. The experimental results achieved a classification accuracy of 94.25%, 87.88%, 89.57%, 96.99%, and 97% with SVM, KNN, BDT, RF, and Ensemble methods, respectively, on FLAIR-, T1C-, and T2-weighted MRI, and the hybrid segmentation attaining 90.16% mean dice score for tumor area segmentation against ground-truth images.