Magnetic resonance brain images classification using linear kernel based Support Vector Machine

The aim of this research paper is the classification of magnetic resonance brain Images as normal and abnormal using linear kernel based Support Vector Machine (SVM) for which different texture features are utilized to characterize the information level contained in the image. The proposed method is compared with k-nearest neighbor (K-NN) classifier and hidden markov model (HMM) classifier. To evaluate the performance of the proposed method, classification rate, recall, and precision evaluation metrics are choosen. The comparative results of the research demonstrates that SVM based on linear kernel provides much higher precision and low error rates as compared to KNN and HMM classifier.