Boundary-Based Feature Extraction and Recognition of Breast Tumors Using Support Vector Machine

Breast cancer is the most common cancer among women. To assist the ultrasound (US) diagnosis of solid breast tumors, the lobulated contour feature quantified by boundary-based corner counts is studied to classify breast tumors as malignant or benign. The corner points in this research was detected based on wavelet transform (WT), and the classification selected through comparison is support vector machine (SVM), with radial based function (RBF) as the kernel function. Experiments were done on a total of 240 cases of breast lesions, including 104 cases of malignant tumors proved at histology and 136 cases of benign tumors. The accuracy of this system is 95.42%, specificity is 98.53% while sensitivity is 91.35%. Consequently, by SVM, the obtained results show that the pro-posed method can be a new intelligent assistance diagnosis.