Detection and classification of masses in breast ultrasound images
Breast cancer can be treated most effectively when detected in its early stage. Due to the superiority to mammography in its ability to detect focal abnormalities in the dense breasts of adolescent women, sonography has become an important adjunct to mammography in breast cancer detection and has been especially useful in distinguishing cysts from solid tumors. In this paper, we develop a novel CAD system based on fuzzy support vector machine to automatically detect and classify mass using ultrasound (US) images. The experimental results show that the proposed system greatly improves the five objective measurements and the area (Az A z ) under the ROC curve compared with those of other classification methods, and radiologist assessments, and the proposed approach will be very valuable for breast cancer control.
breast cancer ultrasound (us) images breast sonography computer-aided diagnosis (cad) feature selection fuzzy support vector machine (fsvm) mass classification