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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
Submitted: March 23, 2012
Accepted: April 23, 2012
Published: December 15, 2012 |
Abstract:
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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.
Key words: Cavernous hemangioma, hepatoma, liver cyst, sentivity, SOM, specificity, SVM
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Cite this Reference:
R. Suganya, A. Hameed Sulthan and S. Rajaram, . Comparison and Retrieval of Liver Diseases Based on the Performance of SVM and SOM. Research Journal of Applied Sciences, Engineering and Technology, (24): 5539-5543.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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