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Title |
A Novel CAD System for Detection and Classification of Liver Cirrhosis using Support Vector Machine and Artificial Neural Network |
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Author |
M.A. Al-Shabi |
| Citation |
Vol. 19 No. 8 pp. 18-23 |
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Abstract |
In this paper, a computer aided system is designed to determine the extent to which the blood indices, fibro-scan and liver biopsy can help diagnose liver cirrhosis in patients with Chronic Hepatitis C. A novel approach, for feature selection is created and used to reduce the extracted features to their best informative subset. The performance of three classifiers is investigated. One is the Support Vector Machine (SVM) with cross-validation, the second is a Multilayer Perception neural network (MLP), and the third is Generalized Regression Neural Network (GRNN). The system resulted in an accuracy of 100% in both training and validation phases for SVM and MLP and 99.50 % for GRNN. |
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Keywords |
Liver Cirrhosis, Artificial Neural Network, Support Vector Machine, Accuracy. |
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