Classification of Tumors in Human Brain MRI using Wavelet and Support Vector Machine

Abstract

The task of classification in recovery systems is to differentiate between normal and abnormal brain. In this paper feature extraction from MR Images is carried out by DAUB-4 Wavelet method. DAUB-4 is an efficient tool for feature extraction because it gives better contrast to an image. Due to better contrast it improves easily hanging signals of an image and reduces the overhead. PCA is used to select the best features for classification. These PCA selected features are given as an input to SVM for classification. In this work we are using two SVM kernel functions which are Linear Kernel and Radial Basis Kernel. Experimental results show that the proposed system have high classification accuracy of 98.7% with Radial Basis Kernel.

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