Comparative Study of Classification System using K-NN, SVM and Ada- boost for Multiple Sclerosis and Tumor Lesions using Brain MRI

Brain Magnetic Resonance Imaging (MRI) plays a very important role for radiologists to diagnose and treat brain tumor/ Multiple Sclerosis (MS) patients. Study of the medical image by the radiologist is a time consuming process and also the accuracy depends upon their experience. Thus, the computer aided systems (CAD) becomes very necessary as they overcome these limitations. This paper presents an automated process of classification of Multiple sclerosis and Tumor lesions from brain MRI in which 3 models for classification of lesions is considered as: i. MS and Normal, ii. MS and Tumor and iii. Benign and Malignant Tumor based on T2-weighted MRI scan. In this work, textural features are extracted using Gray Level Co-occurrence Matrix (GLCM) [13]. Then the classification is done using K-Nearest Neighbor (K-NN), Support Vector Machine (SVM) and Ada-boost classifiers. The performance of the proposed models is evaluated on the basis of accuracy, error rate, sensitivity and specificity. The system performance is also compared with the radiologist’s diagnosis for test samples. The developed CAD system is giving 100% accuracy for all three learning algorithms; with SVM outperforming the K-NN and Ada-boost.