Breast cancer classification of mammographic masses using improved shape features

G. C. Sailesh, Chulwoo Pack, Sung Y. Shin, Hyung Do Choi

Abstract: Breast cancer classification technique divides breast cancer into two categories, benign tumors and malignant tumors. The main purpose of breast cancer classification is to classify abnormalities into benign or malignant classes and thus help physicians with further analysis by minimizing the possible errors that can be done because of fatigued or inexperienced physician. In this paper, we propose three new shape features to classify mammographic images into benign and malignant class. SVM is used as a machine learning tool for training and classification purpose. In order to evaluate the improved performance of the proposed shape features, convexity, circularity and a modified global shape feature of compactness was used. The result shows that the proposed shape features can improve measure of performances such as MCC, specificity, sensitivity and accuracy and can be a promising tool to provide preliminary decision support information to physicians for further diagnosis.