Classification of Mammograms Using Convolutional Neural Network Based Feature Extraction

  • Taye Girma DebeleeEmail author
  • Mohammadreza Amirian
  • Achim Ibenthal
  • Günther Palm
  • Friedhelm Schwenker
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 244)


Breast cancer is the most common cause of death among women in the entire world and the second cause of death after lung cancer. The use of automatic breast cancer detection and classification might possibly enhance the survival rate of the patients through starting early treatment. In this paper, the convolutional Neural Networks (CNN) based feature extraction method is proposed. The features dimensionality was reduced using Principal Component Analysis (PCA). The reduced features are given to the K-Nearest Neighbors (KNN) to classify mammograms as normal or abnormal using 10-fold cross-validation. The experimental result of the proposed approach performed on Mammography Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) datasets were found to be promising compared to previous studies in the area of image processing, artificial intelligence and CNN with an accuracy of 98.75\(\%\) and 98.90\(\%\) on MIAS and DDSM dataset respectively.


Breast cancer Mammogram CNN K-nearest neighbour Feature extraction 


  1. 1.
    American Cancer Society: Breast Cancer Facts and Figures 2015–2016.
  2. 2.
    Sampat, M.P., Markey, M.K., Bovik, A.C.: Computer-aided detection and diagnosis in mammography. In: Handbook of Image and Video Processing. Elsevier Academic Press, San Francisco (2005)CrossRefGoogle Scholar
  3. 3.
    Vedaldi, A., Lenc, K.: MatConvNet-convolutional neural networks for MATLAB. In: Proceedings of the 23rd ACM International Conference on Multimedia, Brisbane, Australia, pp. 689–692 (2015)Google Scholar
  4. 4.
    Torrents-Barrena, J., Puig, D., Ferre, M., Melendez, J., Diez-Presa, L., Arenas, M., Marti, J.: Breast masses identification through pixel-based texture classification. In: Fujita, H., Hara, T., Muramatsu, C. (eds.) IWDM 2014. LNCS, vol. 8539, pp. 581–588. Springer, Cham (2014). Scholar
  5. 5.
    Arevalo, J., González, F.A., Ramos-Pollán, R., Oliveira, J.L., Lopez, M.A.G.: Representation learning for mammography mass lesion classification with convolutional neural networks. Comput. Methods Programs Biomed. 127, 248–257 (2016)CrossRefGoogle Scholar
  6. 6.
    Jiao, Z., Gao, X., Wang, Y., Li, J.: A deep feature based framework for breast masses classification. Neurocomputing 197, 221–231 (2016)CrossRefGoogle Scholar
  7. 7.
    Mahersia, H., Boulehmi, H., Hamrouni, K.: Development of intelligent systems based on Bayesian regularization network and neuro-fuzzy models for mass detection in mammograms: a comparative analysis. Comput. Methods Programs Biomed. 126, 46–62 (2016)CrossRefGoogle Scholar
  8. 8.
    Peng, W., Mayorga, R.V., Hussein, E.M.A.: An automated confirmatory system for analysisof mammograms. Comput. Methods Programs Biomed. 125, 134–144 (2016)CrossRefGoogle Scholar
  9. 9.
    Pratiwi, M., Alexander, Harefa, J., Nandai, S.: Mammograms classification using gray-level co-occurrence matrix and radial basis function neural network. Procedia Comput. Sci. 59, 83–91 (2015)CrossRefGoogle Scholar
  10. 10.
    Dheeba, J., Albert Singh, N., Tamil Selvi, S.: Computer-aided detection of breast cancer on mammograms: a swarm intelligence optimized wavelet neural network approach. J. Biomed. Inform. 49, 45–52 (2014)CrossRefGoogle Scholar
  11. 11.
    Pereira, E.T., Eleutério, S.P., Marques de Carvalho, J.: Local binary patterns applied to breast cancer classification in mammographies. RITA. 21(2), 32–46 (2014)Google Scholar
  12. 12.
    Mammographic Image Analysis: Signs of Diseases.
  13. 13.
    University of South Florida Digital Mammography.
  14. 14.
    The mini-MIAS database of mammograms.
  15. 15.
    Kanadam, K.P., Chereddy, S.R.: Mammogram classification using sparse-ROI: a novel representation to arbitrary shaped masses. Expert Syst. Appl. 57, 204–213 (2016)CrossRefGoogle Scholar
  16. 16.
    Dong, M., Lu, X., Ma, Y., Guo, Y., Ma, Y., Wang, K.: An efficient approach for automated mass segmentation and classification in mammograms. J. Digit. Imaging 28(5), 613–625 (2015)CrossRefGoogle Scholar
  17. 17.
    Rouhi, R., Jafari, M.: Classification of benign and malignant breast tumors based on hybrid level set segmentation. Expert Syst. Appl. 46, 45–59 (2016)CrossRefGoogle Scholar
  18. 18.
    Prasad, S.N., Houserkova, D.: The role of various modalities in breast imaging. Biomed. Pap. Med. Fac. Univ. Palacky Olomouc Czech Repub. 151(2), 209–218 (2007)CrossRefGoogle Scholar
  19. 19.
    Tirona, M.T.: Breast cancer screening update. Am. Fam. Physician 87(2), 274–278 (2011)Google Scholar
  20. 20.
    Palazuelos, G., Trujillo, S., Romero, J.: Breast tomosynthesis: the new age of mammography. Rev. Colomb. Radiol. 25(2), 3926–3933 (2014)Google Scholar
  21. 21.
    Dubey, R.B., Hanmandlu, M., Gupta, S.K.: A comparison of two methods for the segmentation of masses in the digital mammograms. Comput. Med. Imaging Graph. 34, 185–191 (2010)CrossRefGoogle Scholar
  22. 22.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556(2014)Google Scholar
  23. 23.
    Xie, W., Li, Y., Ma, Y.: Breast mass classification in digital mammography based on extreme learning machine. Neurocomputing 173, 930–941 (2016)CrossRefGoogle Scholar
  24. 24.
    Rouhi, R., Jafari, M., Kasaei, S., Keshavrzian, P.: Benign and malignant breast tumor classification based on region growing and CNN segmentation. Expert Syst. Appl. 42, 990–1002 (2015)CrossRefGoogle Scholar
  25. 25.
    Shalev-Shwartz, S., Ben-David, S.: Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, New York (2014)CrossRefGoogle Scholar
  26. 26.
    Convolutional Neural Networks (LeNet).
  27. 27.
    Howard, A.G.: Some improvements on deep convolutional neural network based image classification. CoRR abs/1312.5402(2014)Google Scholar
  28. 28.
    Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. CoRR abs/1311.2901 (2014)Google Scholar
  29. 29.
    Convolutional Neural Networks for Visual Recognition.
  30. 30.
    Bonafede, M.M., Kalra, V.B., Miller, J.D., Fajardo, L.L.: Value analysis of digital breast tomosynthesis for breast cancer screening in a commercially-insured US population. ClinicoEconom. Outcomes Res. 7, 53–63 (2015)Google Scholar
  31. 31.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25, 1097–1105 (2012)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Taye Girma Debelee
    • 1
    • 3
    Email author
  • Mohammadreza Amirian
    • 1
  • Achim Ibenthal
    • 2
  • Günther Palm
    • 1
  • Friedhelm Schwenker
    • 1
  1. 1.Institute of Neural Information ProcessingUlm UniversityUlmGermany
  2. 2.Adama Science and Technology UniversityAdamaEthiopia
  3. 3.Addis Abeba Science and Technology UniversityAddis AbebaEthiopia

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