The manual interpretation of MRI slices based on visual examination by radiologist/physician may lead to missing diagnosis when a large number of MRIs are analyzed. To avoid the human error, an automated intelligent classification system is proposed. This research paper proposes an intelligent classification technique to the problem of classifying four types of brain abnormalities viz. Metastases, Meningiomas, Gliomas, and Astrocytomas. The abnormalities are classified based on Two/Three/ Four class classification using statistical and textural features. In this work, classification techniques based on Least Squares Support Vector Machine (LS-SVM) using textural features computed from the MR images of patient are developed. LS-SVM classifier using non-linear radial basis function (RBF) kernels is compared with other techniques such as SVM classifier and K-Nearest Neighbor (K-NN) classifier. It has been observed that the method proposed using LS-SVM classifier outperforms all the other classifiers tested.
Bioimaging and Biomedical Optics | Biomedical | Computer Engineering | Electrical and Computer Engineering | Signal Processing | Systems and Communications
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Selvi, S. T.,
Least Squares Support Vector Machine Based Classification of Abnormalities in Brain MR Images.
Systems Science, 32(1),