An Artificial Immune System-Based Support Vector Machine Approach for Classifying Ultrasound Breast Tumor Images

作者：Wu Wen Jie; Lin Shih Wei^*; Moon Woo Kyung

来源：Journal of Digital Imaging, 2015, 28(5): 576-585.

DOI：10.1007/s10278-014-9757-1

• Breast tumors
• Textural feature
• Morphological feature
• Artificial immune system algorithm
• Support vector machine

A rapid and highly accurate diagnostic tool for distinguishing benign tumors from malignant ones is required owing to the high incidence of breast cancer. Although various computer-aided diagnosis (CAD) systems have been developed to interpret ultrasound images of breast tumors, feature selection and the setting of parameters are still essential to classification accuracy and the minimization of computational complexity. This work develops a highly accurate CAD system that is based on a support vector machine (SVM) and the artificial immune system (AIS) algorithm for evaluating breast tumors. Experiments demonstrate that the accuracy of the proposed CAD system for classifying breast tumors is 96.67 %. The sensitivity, specificity, PPV, and NPV of the proposed CAD system are 96.67, 96.67, 95.60, and 97.48 %, respectively. The receiver operator characteristic (ROC) area index A(z) is 0.9827. Hence, the proposed CAD system can reduce the number of biopsies and yield useful results that assist physicians in diagnosing breast tumors.

• 出版日期2015-10
• 单位长春大学