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

R. Suganya ； A. Hameed Sulthan ； S. Rajaram

《Research Journal of Applied Sciences, Engineering and Technology》 4卷24期 (2012/12) Pp. 5539-5543

並列摘要

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.

並列關鍵字

Cavernous hemangioma ； hepatoma ； liver cyst ； sentivity ； SOM ； specificity ； SVM