The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Neural Networks: A Comprehensive Foundation
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Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
A New Fuzzy Support Vector Machine Based on the Weighted Margin
Neural Processing Letters
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Fuzzy support vector machine for multi-class text categorization
Information Processing and Management: an International Journal
Fuzzy Support Vector Machine Based on Vague Sets for Credit Assessment
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 01
Computers and Electronics in Agriculture
Image Classification Based on Fuzzy Support Vector Machine
ISCID '08 Proceedings of the 2008 International Symposium on Computational Intelligence and Design - Volume 01
Data Mining with Computational Intelligence
Data Mining with Computational Intelligence
Morlet-RBF SVM model for medical images classification
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Multiple ROI selection based focal liver lesion classification in ultrasound images
Expert Systems with Applications: An International Journal
The Visual Computer: International Journal of Computer Graphics
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In this study, we present an application of fuzzy support vector machine (FSVM) and image processing techniques for identifying liver tumor, including malignant and benign tumors. The gray level co-occurrence matrix (GLCM) matrices were utilized to evaluate the texture features of the regions of interest (ROI) of sonography in our experiment. Five textural features: energy, contrast, correlation, entropy, and homogeneity were extracted from the liver segmented images and analyzed using the texture average of four directions (0^o, 45^o, 90^o, 135^o) and distance, @d=6. The proposed system adopts the FSVM to distinguish between malignant and benign tumor cases more efficiently than support vector machine (SVM). The Gaussian RBF kernel has been used be more suitable for the application of identifying liver tumor from B-Mode ultrasound images than polynomial learning machine kernel and linear network kernel. The values of the parameters gamma (g) and regularization parameter (C) have been selected as 0.29 and 4.31x10^3, respectively. Via testing over 200 test cases by using RBF kernel, an overall accuracy of 97.0% has been received by the proposed FSVM algorithm. FSVM (A"Z=0.984+/-0.014) obtain a better result than SVM (A"Z=0.963+/-0.017) in recognition. It is demonstrated that the proposed FSVM algorithm and GLCM texture features technique are feasible and excellent in ultrasonography classification of liver tumor.