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Computational and Mathematical Methods in Medicine
Volume 2017, Article ID 4896386, 7 pages
https://doi.org/10.1155/2017/4896386
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.

Linked References

  1. R. L. Siegel, K. D. Miller, and A. Jemal, “Cancer statistics, 2015,” A Cancer Journal for Clinicians, vol. 63, no. 1, pp. 11–30, 2014. View at Google Scholar
  2. W. Chen, R. Zheng, P. D. Baade et al., “Cancer statistics in China, 2015,” A Cancer Journal for Clinicians, vol. 66, no. 2, pp. 115–132, 2016. View at Publisher · View at Google Scholar · View at Scopus
  3. X. Liu and Z. Zeng, “A new automatic mass detection method for breast cancer with false positive reduction,” Neurocomputing, vol. 152, pp. 388–402, 2015. View at Publisher · View at Google Scholar · View at Scopus
  4. N. Kiarashi, J. Y. Lo, Y. Lin et al., “Development and application of a suite of 4-D virtual breast phantoms for optimization and evaluation of breast imaging systems,” IEEE Transactions on Medical Imaging, vol. 33, no. 7, pp. 1401–1409, 2014. View at Publisher · View at Google Scholar · View at Scopus
  5. N. Saidin, H. A. M. Sakim, U. K. Ngah, and I. L. Shuaib, “Computer aided detection of breast density and mass, and visualization of other breast anatomical regions on mammograms using graph cuts,” Computational & Mathematical Methods in Medicine, vol. 2013, Article ID 205384, 13 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  6. D. S. M. Buist, P. L. Porter, C. Lehman et al., “Factors contributing to mammography failure in women aged 40–49 years,” Journal of the National Cancer Institute, vol. 16, no. 4, pp. 323-324, 2006. View at Google Scholar
  7. X. Xi, H. Xu, H. Shi et al., “Robust texture analysis of multi-modal images using local structure preserving ranklet and multi-task learning for breast tumor diagnosis,” Neurocomputing, 2017. View at Publisher · View at Google Scholar
  8. H. D. Cheng, J. Shan, W. Ju, Y. Guo, and L. Zhang, “Automated breast cancer detection and classification using ultrasound images: a survey,” Pattern Recognition, vol. 43, no. 1, pp. 299–317, 2010. View at Publisher · View at Google Scholar · View at Scopus
  9. W. Gómez, W. C. A. Pereira, and A. F. C. Infantosi, “Analysis of co-occurrence texture statistics as a function of gray-level quantization for classifying breast ultrasound,” IEEE Transactions on Medical Imaging, vol. 31, no. 10, pp. 1889–1899, 2012. View at Publisher · View at Google Scholar · View at Scopus
  10. P. Crystal, S. D. Strano, S. Shcharynski, and M. J. Koretz, “Using sonography to screen women with mammographically dense breasts,” American Journal of Roentgenology, vol. 181, no. 1, pp. 177–182, 2003. View at Publisher · View at Google Scholar · View at Scopus
  11. K. M. Kelly, J. Dean, S.-J. Lee, and W. S. Comulada, “Breast cancer detection: radiologists' performance using mammography with and without automated whole-breast ultrasound,” European Radiology, vol. 20, no. 11, pp. 2557–2564, 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. P. Skaane, R. Gullien, E. B. Eben, M. Sandhaug, R. Schulz-Wendtland, and F. Stoeblen, “Interpretation of automated breast ultrasound (ABUS) with and without knowledge of mammography: A reader performance study,” Acta Radiologica, vol. 56, no. 4, pp. 404–412, 2015. View at Publisher · View at Google Scholar · View at Scopus
  13. I. Torjesen, “Adding ultrasound to mammography could increase breast cancer detection in Asian women,” British Medical Journal, vol. 351, Article ID h5926, 2015. View at Publisher · View at Google Scholar · View at Scopus
  14. P. Skaane, “Ultrasonography as adjunct to mammography in the evaluation of breast tumors,” Acta Radiologica Supplementum, vol. 420, no. 420, pp. 1–47, 1999. View at Google Scholar
  15. J. L. Jesneck, J. Y. Lo, and J. A. Baker, “Breast mass lesions: computer-aided diagnosis models with mammographic and sonographic descriptors,” Radiology, vol. 244, no. 2, pp. 390–398, 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. Z.-H. Zhou, J. Wu, and W. Tang, Ensembling neural networks: many could be better than all [M.S. thesis], Elsevier Science Publishers Ltd, Amsterdam, Holland, 2002. View at Publisher · View at Google Scholar · View at MathSciNet
  17. C.-M. Chen, Y.-H. Chou, K.-C. Han et al., “Breast lesions on sonograms: computer-aided diagnosis with nearly setting-independent features and artificial neural networks,” Radiology, vol. 226, no. 2, pp. 504–514, 2003. View at Publisher · View at Google Scholar · View at Scopus
  18. G. Giacinto and F. Roli, “Design of effective neural network ensembles for image classification purposes,” Image and Vision Computing, vol. 19, no. 9-10, pp. 699–707, 2001. View at Publisher · View at Google Scholar · View at Scopus