SVM for Classification of Brain MRI with K-Means for Detection

SVM for Classification of Brain MRI with K-Means for Detection

D Kalpana

Assistant Professor
Dept.of CSE(DS)
Malla Reddy Engineering College

Hyderabad, India
kasamkalpana@gmail.com

K Sudhakar Reddy
Associate Professor

Dept.of Information Technology
Malla Reddy College of Engineering and Technology
Hyderabad, India
kasamsudha86@gmail.com

Abstract— The segmentation of MR brain images using this method is based on K-means clustering, feature extraction using a discrete wavelet transform (DWT), and feature selection using a grey level co-occurrence matrix (GLCM). For this procedure, we have used a Perfect Radial Basis Function (RBF) - Support Vector Machine (SVM) Classifier. Based on fractions of selectivity and sensitivity, the classifier's performance was measured in terms of accuracy. The proposed classifier was found to be 93% accurate. Additionally, the Histogram methodology was applied in this proposed method in place of randomly choosing the cluster centres.

Keywords: K-means clustering, Histon creation, Discrete Wavelet Transform (DWT), GLCM feature selection, and RBF-SVM Classifier.