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

Wen Jie Wu, Shih Wei Lin*, Woo Kyung Moon

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

26 Scopus citations

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 Az 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.

Original languageEnglish
Pages (from-to)576-585
Number of pages10
JournalJournal of Digital Imaging
Volume28
Issue number5
DOIs
StatePublished - 23 10 2015
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2014, Society for Imaging Informatics in Medicine.

Keywords

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

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