RETRACTED ARTICLE: Effective segmentation and classification of brain tumor using rough K means algorithm and multi kernel SVM in MR images

Abstract

From the classifications, an effective brain tumor classification and segmentation is the curious part for identifying the tumor and non-tumor cells in brain and the cell levels are evaluated. The brain tumor segmentation and classification is established on their experiences. The accuracy of tumor segmentation is very crucial to diagnosis accuracy. So, in our work we are align and improve an approach for tumor identification applying brain MR image segmentation. With an efficient, accurate and reproducible manner, the aim of our suggested method is to evaluate the tumor. Then the brain tumor is separated by using the effective techniques. For segmentation process, first the MRI image must be preprocessed. Next, the process of feature extraction is done by using preprocessed images. In feature extraction process, a raised Gabor wavelet transform (IGWT) is applied. In this research, the means of optimization technique is changed from the traditional Gabor wavelet transform. And the effectiveness of that optimization technique is aligned by using an oppositional fruit fly algorithm. At the end of the process, feature values are transferred in to the clustering process for segmentation. In this article we are introduced an algorithm called as rough k means clustering algorithm for segmentation. Here, we are applying an oppositional fruit fly algorithm to develop an effectiveness of the Gabor filter. Further to raise the classification accuracy of brain tumor we are introduced a multi kernel support vector machine algorithm.