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Computational and Mathematical Methods in Medicine
Volume 2017, Article ID 4896386, 7 pages
Research Article

A Selective Ensemble Classification Method Combining Mammography Images with Ultrasound Images for Breast Cancer Diagnosis

1School of Information Science and Engineering, Key Lab of Intelligent Computing & Information Security in Universities of Shandong, Institute of Life Sciences, Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, and Key Lab of Intelligent Information Processing, Shandong Normal University, Jinan 250358, China
2College of Science and Technology, Shandong University of Traditional Chinese Medicine, Jinan 250014, China
3School of Computer Science and Technology, Shandong University, Jinan 250100, China

Correspondence should be addressed to Benzheng Wei; moc.anis@99zbw and Yuanjie Zheng; moc.liamg@eijnauygnehz

Received 21 March 2017; Accepted 28 May 2017; Published 27 June 2017

Academic Editor: Po-Hsiang Tsui

Copyright © 2017 Jinyu Cong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Breast cancer has been one of the main diseases that threatens women’s life. Early detection and diagnosis of breast cancer play an important role in reducing mortality of breast cancer. In this paper, we propose a selective ensemble method integrated with the KNN, SVM, and Naive Bayes to diagnose the breast cancer combining ultrasound images with mammography images. Our experimental results have shown that the selective classification method with an accuracy of 88.73% and sensitivity of 97.06% is efficient for breast cancer diagnosis. And indicator presents a new way to choose the base classifier for ensemble learning.