Classification of Brain Tumor in Magnetic Resonance Images using Hybrid Kernel based Support Vector Machine
Arun. R, Singaravelan. S, Murugan. D
Journal of Multimedia Processing and Technologies, Vol. 11, No. 1, 2020.
DOI: https://doi.org/10.6025/jmpt/2020/11/1/11-22
Link: http://www.dline.info/jmpt/fulltext/v11n1/jmptv11n1_2.pdf
Abstract:
Medical image segmentation is a knotty and challenging task. Predominantly, the brain has a complicated
structure and its exact segmentation is very essential for identifying the tumors, edema, and necrotic tissues in order to provide proper treatment. In this paper, we have proposed a novel brain tumor classification of MR images using texture features and hybrid kernel based SVM. Our proposed approach comprises the following major steps: i) preprocessing ii) Tumor Region Location iii) Feature Extraction and iv) Final Classification. In preprocessing steps, Anisotropic filtering will be applied to diminish the noise and improved quality of the image for further processing. In the next steps to perform the
skull stripping and tumor regions are identified using regionprops algorithm. In feature extraction some specific feature will be extracted using texture using GLCM (Gray Level Co-occurrence Matrix). In the classification stage, the hybrid kernel will be designed and apply to training of support vector machine (SVM) to perform automatic detection of tumor in MRI images. For comparative analysis, our proposed approach is compared with the existing works. The accuracy level (93%) for our proposed approach is proved is good at detecting the tumors in the brain MRI images.