SVM Kernels comparison for brain tumor diagnosis using MRI
Department of computer science, University Al Mustansiriyah, Baghdad, Iraq.
Research Article
Global Journal of Engineering and Technology Advances, 2021, 07(02), 026-036.
Article DOI: 10.30574/gjeta.2021.7.2.0065
Publication history:
Received on 30 March 2021; revised on 02 May 2021; accepted on 05 May 2021
Abstract:
Magnetic Resonance Image (MRI) brain images have an essential role in medical analysis and cancer identification .In this paper multi kernel SVM algorithm is used for MRI brain tumor detection. The proposed work is involving the following stages: image acquisition, image preprocessing, feature extraction and tumor classification. An automatic threshold selection region based segmentation method called Otsu is used for thresholding during preprocessing stage. SVM classification algorithm with four different kernels are used to determine the normal and abnormal images. SVM with quadratic kernel results in best classification accuracy of 86.5%.
Keywords:
Brain tumors; Support vector machine; Kernels; Image processing; Magnetic Resonance Image (MRI)
Full text article in PDF:
Copyright information:
Copyright © 2021 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0