SVM-PUK kernel based MRI-brain tumor identification using texture and Gabor wavelets

Abstract

In this study, we propose an efficient method to identify unwanted growth in brain using SVM-PUK on convoluted textural features with reduced Gabor wavelet features. After preprocessing, GLCM features of image are extracted and further, convoluted with reduced Gabor features using PCA of the image. Then, the convoluted GLCM features and reduced Gabor features classified with the SVM using PUK kernel. The proposed method performance is evaluated on BRATS’18 database and achieved an accuracy of 91.31 % in recognizing the effected tissues, and shown better performance over ED, DTW, FFNN and PNN.