A NOVEL APPROACH FOR THE EXTRACTION AND CLASSIFICATION OF TUMOR IN MR IMAGES OF THE BRAIN VIA PRINCIPLE COMPONENT ANALYSIS AND KERNEL SUPPORT VECTOR MACHINE

Abstract

Medical imaging plays an important role in the medical field. In present time there are various computerized methods for medical imaging to diagnose the inner portion of human body. Brain tumor detection is an important application in recent days. In this paper, various methods have been used for brain tumor detection and classification taking magnetic resonance images as input. For tumor classification, we had done experimentation with 50 MRI images taken from “figshare brain data set”. We propose an efficient method for brain tumor classification to classify cancerous and noncancerous tumor. The proposed method has three major steps 1.) Feature extraction 2) Feature reduction and 3) Classification. The present approach extracts the statistical texture features using 2D Discrete Wavelet transformation (Daubechies) and GLCM. PCA (principle Component Analysis) is used for feature reduction. We create a training data set that was carried out with 50 MRI images and applied SVM classifier to classify the tumor whether it is Benign and Malignant and evaluate the performance by Kernel based SVM.