Detection of brain tumor using optimized fuzzy C-means and SVM classifier

The massive growth of abnormal brain cells leads to Brain tumor. The accurate detection of brain tumor is thus imperative. In this paper, to obtain satisfactory segmentation of brain MRI image, optimized Fuzzy C means clustering is used. The proposed method consists of various steps. First, to upgrade the quality of brain MRI image, contrast enhancement is used. For skull stripping, morphological operations are presented. Second, its result is fed to segmentation stage, where optimized Fuzzy C means clustering with genetic algorithm is utilized. Next, feature extraction with combination of GLRLM and GLCM method is proposed. The SVM classifier, based on extracted features, classifies tumor and normal brain images. The training of SVM is carried out using 150 MRI images dataset. More accurate results are obtained using 5 kernels methods that are Linear, Radial Basis Function, Quadratic, Polynomial and MLP kernel. The highest accuracy of 94.8% is obtained using quadratic kernel function. Successfully, proposed method amends rate of accuracy and identifies tumor and non-tumor images.