Detection and Classification of Brain Tumor using KSVM

  • Ravendra Singh Dr. Bharat Bhushan Agarwal
Keywords: : K-Nearest Neighbour (KNN), Magnetic Resonance Imaging, Support Vector Machine (SVM), Radial Basis Function (RBF)

Abstract

The proposed work presents a method for automatic classification of medical images in two classes Normal and Abnormal based on image features and automatic abnormality detection. Our proposed system consists of four phases Preprocessing, Feature extraction, Classification. Statistical texture feature set is de-rived from normal and abnormal images. We used the KSVM classifier for classifying image. The KNN classifier performance compared with kernel based SVM classifier (Linear and RBF). The confusion matrix computed and result shows that KSVM obtain above 90% classification rate which is more than KNN classification rate. So, we choose KSVM algorithm for classification of images. The system has been tested on the number of MRI brain images.

Published
2021-10-16
How to Cite
Dr. Bharat Bhushan Agarwal, R. S. (2021). Detection and Classification of Brain Tumor using KSVM . Design Engineering, 4671-4678. Retrieved from http://www.thedesignengineering.com/index.php/DE/article/view/5420
Section
Articles