An identification method of malignant and benign liver tumors from ultrasonography based on GLCM texture features and fuzzy SVM

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°, 45°, 90°, 135°) and distance, δ = 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 4.31 × 103, 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 (AZ = 0.984 ± 0.014) obtain a better result than SVM (AZ = 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.