Support vector machine based discrete wavelet transform for magnetic resonance imaging brain tumor classification

Here, a brain tumor classification method using the support vector machine (SVM) algorithm by utilizing discrete wavelet transform (DWT) transformation and feature extraction of gray-level co-occurrence matrix (GLCM) local binary pattern (LBP) has been implemented magnetic resonance imaging (MRI) image belong to low-grade glioma (LGG) or high-grade (HGG) group. SVM used as widely in research that raises topic classification. Through formation hyperplane between 2 data classes, can be said reliable but does not require complicated computations. The DWT is intended provide clearer details from MRI image, so when applied, it expected extracted features will differ benign images malignant images. In 1 level high-low (HL) sub-band yield highest specificity, sensitivity, accuracy than 3 levels HL low-high (LH) LGG image.Compared with another research, our proposed slightly better terms classify achieved 98.6486%.