Brain Tumor Image Classification Using Gaussian Model Based on Machine Learning Based on MRI Dataset

Abstract

Early detection of brain tumors using brain magnetic resonance imaging is needed to prevent benign tumors from developing into malignant tumors. This study aims to classify brain tumors using thresholding and support vector machine (SVM) methods. The thresholding methods used in this study are global thresholding, adaptive thresholding and gaussian thresholding. The evaluation methods used are accuracy, recall, precision, and specificity. This study has used magnetic resonance imaging (MRI) based image datasets totaling 3,079 data. Overall, the accuracy of the support vector machine (SVM) algorithm and adaptive thresholding method got the best accuracy of 84.25%, while the gaussian thresholding method got 82.81% accuracy and global thresholding got 81.25% accuracy.