Breast Cancer Medical Image Analysis Based on Transfer Learning Model

  • Yi LiuEmail author
  • Xiaolong Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10955)


Breast cancer becomes one of the common cancer among women. The computer-aided diagnosis is a very available technology, and it becomes an inevitable trend of modern medicine. There is litter data in the medical field which is not enough to support training model. The paper aims to solve the problem in the case of insufficient data volume, and a transfer learning model combined with Convolutional Neural Network (CNN) is proposed to achieve the goal. This model has three innovations. The first one takes Xavier method to initialize parameter, which can make the training process more stable. The second innovation takes dropout method to discard some network nodes randomly, which can reduce overfitting problem. The third one adds two convolutional layers and one max pooling layer before final fully connected layer. The experimental results have shown that this strategy is suitable for the problem in this paper. The paper indicates that the transfer learning model is an effective method with small-scale data, and it can be combined with deep learning algorithms.


Breast cancer Transfer learning Convolutional Neural Network 



This work was supported in part by National Natural Science Foundation of China (61273225, 61373109, 61702381).


  1. 1.
    Veronesi, U., Boyle, P., Goldhirsch, A., et al.: Breast cancer. Lancet 365(9472), 1727–1741 (2015)CrossRefGoogle Scholar
  2. 2.
    Kelsey, J.L., Hornross, P.L.: Breast cancer: magnitude of the problem and descriptive epidemiology. Epidemiol. Rev. 15(1), 7 (1993)CrossRefGoogle Scholar
  3. 3.
    Heath, M., Bowyer, K., Kopans, D., et al.: The digital database for screening mammography. In: Digital Mammography, pp. 457–460. Springer, Netherlands (2001)Google Scholar
  4. 4.
    Sucking, J., Boggis, C.R.M., Hutt, I., et al.: The Mini-MIAS database of mammograms. In: International Congress Series, vol. 1069, pp. 375–378 (1994)Google Scholar
  5. 5.
    Wei, L., Yang, Y., Nishikawa, R.M., et al.: A study on several Machine-learning methods for classification of malignant and benign clustered microcalcifications. IEEE Trans. Med. Imaging 24(3), 371–380 (2005)CrossRefGoogle Scholar
  6. 6.
    Ren, J., Wang, D., Jiang, J.: Effective recognition of MCCs in mammograms using an improved neural classifier. Eng. Appl. Artif. Intell. 24(4), 638–645 (2011)CrossRefGoogle Scholar
  7. 7.
    Sun, D., Wang, M., Li, A.: A multimodal deep neural network for human breast cancer prognosis prediction by integrating multi-dimensional data. IEEE/ACM Trans. Comput. Biol. Bioinform. 1 (2018)Google Scholar
  8. 8.
    Shen, L., Rangayyan, R.M., Desautels, J.E.L.: Application of shape analysis to mammographic calcifications. IEEE Trans. Med. Imaging 13(2), 263–274 (1994)CrossRefGoogle Scholar
  9. 9.
    Ma, Y., Tay, P.C., Adams, R.D., et al.: A novel shape feature to classify microcalcifications. In: IEEE International Conference on Image Processing, Western Carolina University, pp. 2265–2268. IEEE (2010)Google Scholar
  10. 10.
    Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)CrossRefGoogle Scholar
  11. 11.
    Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)CrossRefGoogle Scholar
  12. 12.
    Szegedy, C., Liu, W., Jia, Y., et al.: Going Deeper with Convolutions, pp. 1–9 (2014)Google Scholar
  13. 13.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems, pp. 1097–1105. Curran Associates Inc. (2012)Google Scholar
  14. 14.
    Doi, K.: Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput. Med. Imaging Graph. 31(4–5), 198–211 (2007)CrossRefGoogle Scholar
  15. 15.
    Deng, J., Dong, W., Socher, R., et al.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 248–255. IEEE (2009)Google Scholar
  16. 16.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Computer Science, pp. 1–15 (2014)Google Scholar
  17. 17.
    Russakovsky, O., Deng, J., Su, H., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. J. Mach. Learn. Res. 9, 249–256 (2010)Google Scholar
  19. 19.
    Srivastava, N., Hinton, G., Krizhevsky, A., et al.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)MathSciNetzbMATHGoogle Scholar

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyWuhan University of Science and TechnologyWuhanChina
  2. 2.Intelligent Information Processing and Real-Time Industrial Systems Hubei Province Key LaboratoryWuhanChina

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