Effective MKSVM Classifier with LDA Methods for Brain Tumor Detection in MR Images

In recent times, one of the perilous diseases which cause to increase the patient’s death rate is a brain tumor. For diagnosing the tumor diseases from Magnetic Resonance Images (MRI), different classification methods have been analyzed. This paper presented an innovative method to diagnose brain tumor disease by using the classification process in MRI. From the input MR images, brain tumor image is classified by the supervised learning classifier i.e. Multi Kernel Support Vector machine (MKSVM). This model incorporates the extraction of feature and reduction process. All the MRI brain images are considered to extract some standard features and reduction reason dimensionality reduction that is Linear Discriminant Analysis (LDA) applied. Reason for this technique to the removal of multi-collinearity enhances the execution of the proposed model. Utilizing the feature vector attained out of the MRI images, SVM classifiers are utilized to image classification. The procedure comprises of two parts that are training stage as well as a testing stage. Parameters used to analyze the classified images as sensitivity, specificity, accuracy and so on. A cross-validation plot is adopted to enhance the generalization capability of the framework