Brain Tumor Classification using Supervised Support Vector Machine

Abstract

A Glioblastoma (GBM) is a malignant brain tumor earlier detection and diagnosis will increase survival opportunities. This research has developed for binary classification of GBM brain tumors by supervised machine learning from magnetic resonance imaging (MRI). DICOM medical images have been converted into JPEG files and morphological operation has been implemented to separate the brain region from the skull image for preparation and easier for tumor segmentation in preprocessing stage. The global thresholding segmentation has been proposed to segment the brain tumor from the artifact and then the features have been extracted by gray level coefficient matrix feature extraction (GLCM). In this research, a support vector machine has been conducted for binary classification and finally, GBM grade-4 brain tumor is distinguished from normal brain images. The dataset comprises 155 MRI images 80% has been assigned for training and another 20% will be the testing dataset. The experimental output prediction result is 96.875 % accuracy, 95 % sensitivity, and 100% specificity. The performance of classification has been improved and shown better results when compared with previous research work.