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2012 (Vol. 4, Issue: 24)
Article Information:

Comparison and Retrieval of Liver Diseases Based on the Performance of SVM and SOM

R. Suganya, A. Hameed Sulthan and S. Rajaram
Corresponding Author:  R. Suganya 

Key words:  Cavernous hemangioma, hepatoma, liver cyst, sentivity, SOM, specificity, SVM
Vol. 4 , (24): 5539-5543
Submitted Accepted Published
March 23, 2012 April 23, 2012 December 15, 2012

In this study, we distinguish the liver tumor by SVM and SOM classification. LPND (Laplacian Pyramid based Nonlinear DiTusion) is the proposed speckle reduction technique for preprocessing the image. In Feature extraction, we segment the image based on mean, variance, entropy and fractal dimension. The four layer hierarchical scheme is used for classifying benign and malignant tumors. In the Wrst layer the normal tissue distinguishes from abnormal tissues. The second layer distinguishes cyst from abnormal tissues. Cavernous Hemangioma is identiWed in third layer. At last hepatoma is identiWed from undeWned tissues. Self Organizing Map (SOM) and Support Vector Machine (SVM) algorithms are used to classify the features extracted from liver diseases. Using performance metrics such as sensitivity and specificity, our results demonstrate that the SVM provide better retrieval than SOM.
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  Cite this Reference:
R. Suganya, A. Hameed Sulthan and S. Rajaram, 2012. Comparison and Retrieval of Liver Diseases Based on the Performance of SVM and SOM.  Research Journal of Applied Sciences, Engineering and Technology, 4(24): 5539-5543.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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