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A Kernel SVM Classifier for Classification of Brain Tumors in Magnetic Resonance Images

T. Chandra Sekhar Rao*, G.Sreenivasulu**

* Professor, Department of Electronics and Communication Engineering, Loyola Institute of Technology and Management, Sattenapalli, India.

** Professor, Department of Electronics and Communication Engineering, SVU College of Engineering, Tirupati, India.

Periodicity:July - September'2016

Abstract

The term Computer Aided Diagnosis (CAD) broadly encompasses the use of computer algorithms to aid in the process of image interpretation. CAD is also now used in general to categorize and computerize the extraction of quantitative measurements from medical images. CAD system has become the most important research subject in the domain of medical imaging and diagnostic radiology. CAD systems act as a credible secondary opinion thereby improving the accuracy and the consistency of radiological diagnosis. In this work a classifier based on Support Vector Machine (SVM) has been designed and presented for the classification of brain tumors in images from Magnetic Resonance Imaging (MRI). The SVM classifier uses a kernel in the form of Gaussian Radial Basis function kernel (GRB kernel) to enhance the classifier performance. The result of the classifier performance has been validated with the help of expert clinical opinion. The results demonstrate the suitability of the proposed classifier in the classification of brain tumors.

Keywords

CAD, SVM, Kernel, MRI, GRB.

How to Cite this Article?

Rao, T. C. S., and Sreenivasulu, G. (2016). A Kernel SVM Classifier for Classification of Brain Tumors in Magnetic Resonance Images. i-manager's Journal on Image Processing, 3(3), 34-41.

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