Least squares support vector machine based classification of abnormalities in brain MR images

• Autorzy: Selvi S. , Selvathi D. , Ramkumar R. , Selvaraj H.
• Języki publikacji: EN

Abstrakty

EN

The manual interpretation of MRI slices based on visual examination by radiologist/physician may lead to missing diagnosis when a large number of MRJs arc 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 texlural 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-Ncarest Neighbor (K-NN) classifier. It has been observed that the method proposed using LS-SVM classifier outperforms all the other classifiers tested.

Słowa kluczowe

EN

medical images; magnetic resonance imaging; X-rays; intelligent classification technique

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Systems Science
• Rocznik: 2006
• Tom: Vol. 32, no 1
• Strony: 89--103
• Opis fizyczny: Bibliogr. 18 poz.

Twórcy

autor

Selvi S.

autor

Selvathi D.

autor

Ramkumar R.

autor

Selvaraj H.

• Anna University, MIT Campus, Chennai, India

Bibliografia

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