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

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Abstract

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.

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Acknowledgements

The first author of this paper is grateful to the Ministry of Science and Technology of the Republic of China (Taiwan) for financially supporting this research [grant number NSC99-2221-E-182-041]. The second author is grateful to the Ministry of Science and Technology of the Republic of China (Taiwan) and the Linkou Chang Gung Memorial Hospital for financially supporting this research [grant numbers MOST103-2410-H-182-006 and CARPD3B0012].

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Authors and Affiliations

  1. Department of Information Management, Chang Gung University, Tao-Yuan, Taiwan, 333, Republic of China

    Wen-Jie Wu & Shih-Wei Lin

  2. Department of Diagnostic Radiology, Seoul National University Hospital, Seoul, South Korea

    Woo Kyung Moon

Authors
  1. Wen-Jie Wu
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  2. Shih-Wei Lin
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  3. Woo Kyung Moon
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Correspondence to Shih-Wei Lin.

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Wu, WJ., Lin, SW. & Moon, W.K. An Artificial Immune System-Based Support Vector Machine Approach for Classifying Ultrasound Breast Tumor Images. J Digit Imaging 28, 576–585 (2015). https://doi.org/10.1007/s10278-014-9757-1

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  • DOI: https://doi.org/10.1007/s10278-014-9757-1

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