Classification of Breast Masses Using Convolutional Neural Network as Feature Extractor and Classifier

  • Pinaki Ranjan Sarkar
  • Deepak Mishra
  • Gorthi R. K. Sai Subrahmanyam
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 704)

Abstract

Due to the difficulties of radiologists to detect micro-calcification clusters, computer-aided detection (CAD) system is much needed. Many researchers have undertaken the challenge of building an efficient CAD system and several feature extraction methods are being proposed. Most of them extract low- or mid-level features which restrict the accuracy of the overall classification. We observed that high-level features lead to a better diagnosis and convolutional neural network (CNN) is the best-known model to extract high-level features. In this paper, we propose to use a CNN architecture to do both of the feature extraction and classification task. Our proposed network was applied to both MIAS and DDSM databases, and we have achieved accuracy of \(99.074\%\) and \(99.267\%\), respectively, which we believe that is the best reported so far.

Keywords

Micro-calcification Computer-aided detection Convolutional neural network High-level features 

References

  1. 1.
    Globocan project 2012. International Agency for Research on Cancer, http://globocan.iarc.fr/
  2. 2.
  3. 3.
    Abdel-Zaher, A.M., Eldeib, A.M.: Breast cancer classification using deep belief networks. Expert Systems with Applications 46, 139–144 (2016)Google Scholar
  4. 4.
    Arevalo, J., González, F.A., Ramos-Pollán, R., Oliveira, J.L., Lopez, M.A.G.: Convolutional neural networks for mammography mass lesion classification. In: Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE. pp. 797–800. IEEE (2015)Google Scholar
  5. 5.
    Beura, S., Majhi, B., Dash, R.: Mammogram classification using two dimensional discrete wavelet transform and gray-level co-occurrence matrix for detection of breast cancer. Neurocomputing 154, 1–14 (2015)Google Scholar
  6. 6.
    Bird, R.E., Wallace, T.W., Yankaskas, B.C.: Analysis of cancers missed at screening mammography. Radiology 184(3), 613–617 (1992)Google Scholar
  7. 7.
    Dhungel, N., Carneiro, G., Bradley, A.P.: A deep learning approach for the analysis of masses in mammograms with minimal user intervention. Medical image analysis 37, 114–128 (2017)Google Scholar
  8. 8.
    Ertosun, M.G., Rubin, D.L.: Probabilistic visual search for masses within mammography images using deep learning. In: Bioinformatics and Biomedicine (BIBM), 2015 IEEE International Conference on. pp. 1310–1315. IEEE (2015)Google Scholar
  9. 9.
    Görgel, P., Sertbas, A., Ucan, O.N.: Mammographical mass detection and classification using local seed region growing–spherical wavelet transform (lsrg–swt) hybrid scheme. Computers in biology and medicine 43(6), 765–774 (2013)Google Scholar
  10. 10.
    Jiao, Z., Gao, X., Wang, Y., Li, J.: A deep feature based framework for breast masses classification. Neurocomputing 197, 221–231 (2016)Google Scholar
  11. 11.
    Jona, J., Nagaveni, N.: A hybrid swarm optimization approach for feature set reduction in digital mammograms. WSEAS Transactions on Information Science and Applications 9, 340–349 (2012)Google Scholar
  12. 12.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. pp. 1097–1105 (2012)Google Scholar
  13. 13.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)Google Scholar
  14. 14.
    LeCun, Y., Kavukcuoglu, K., Farabet, C.: Convolutional networks and applications in vision. In: Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium on. pp. 253–256. IEEE (2010)Google Scholar
  15. 15.
    Ramirez-Villegas, J.F., Ramirez-Moreno, D.F.: Wavelet packet energy, tsallis entropy and statistical parameterization for support vector-based and neural-based classification of mammographic regions. Neurocomputing 77(1), 82–100 (2012)Google Scholar
  16. 16.
    Rebecca Sawyer Lee, Francisco Gimenez, A.H., Rubin, D.: Curated breast imaging subset of ddsm. The Cancer Imaging Archive,  https://doi.org/10.7937/K9/TCIA.2016.7O02S9CY
  17. 17.
    Roth, H.R., Lu, L., Liu, J., Yao, J., Seff, A., Cherry, K., Kim, L., Summers, R.M.: Improving computer-aided detection using convolutional neural networks and random view aggregation. IEEE transactions on medical imaging 35(5), 1170–1181 (2016)Google Scholar
  18. 18.
    Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2017. CA: A Cancer Journal for Clinicians 67(1), 7–30 (2017),  https://doi.org/10.3322/caac.21387
  19. 19.
    Suckling, J., Parker, J., Dance, D., Astley, S., Hutt, I., Boggis, C., Ricketts, I., Stamatakis, E., Cerneaz, N., Kok, S., et al.: The mammographic image analysis society digital mammogram database. In: Exerpta Medica. International Congress Series. vol. 1069, pp. 375–378 (1994)Google Scholar
  20. 20.
    Wang, Y., Li, J., Gao, X.: Latent feature mining of spatial and marginal characteristics for mammographic mass classification. Neurocomputing 144, 107–118 (2014)Google Scholar
  21. 21.
    Xie, W., Li, Y., Ma, Y.: Breast mass classification in digital mammography based on extreme learning machine. Neurocomputing 173, 930–941 (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Pinaki Ranjan Sarkar
    • 1
  • Deepak Mishra
    • 1
  • Gorthi R. K. Sai Subrahmanyam
    • 2
  1. 1.Indian Institute of Space Science and TechnologyThiruvananthapuramIndia
  2. 2.Indian Institute of Technology TirupatiTirupatiIndia

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