Support Vector Machine Based Automatic Classification of Human Brain Using MR Image Features

Document Type



This paper proposes an intelligent classification technique to identify two categories of MRI volume data as normal and abnormal. The manual interpretation of MRI slices based on visual examination by radiologist/physician may lead to incorrect diagnosis when a large number of MRIs are analyzed. In this work, the textural features are extracted from the MR data of patients and these features are used to classify a patient as belonging to normal (healthy brain) or abnormal (tumor brain). The categorization is obtained using various classifiers such as support vector machine (SVM), radial basis function, multilayer perceptron and k-nearest neighbor. The performance of these classifiers are analyzed and a quantitative indication of how better the SVM performance is when compared with other classifiers is presented. In intelligent computer aided health care system, the proposed classification system using SVM classifier can be used to assist the physician for accurate diagnosis.


Artificial intelligence; Classification--Computer programs; Image analysis--Data processing; Magnetic resonance imaging; Support vector machines


Bioimaging and Biomedical Optics | Biomedical | Computer Engineering | Electrical and Computer Engineering | Signal Processing | Systems and Communications


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