Deep Layer CNN Architecture for Breast Cancer Histopathology Image Detection

  • Zanariah ZainudinEmail author
  • Siti Mariyam Shamsuddin
  • Shafaatunnur Hasan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 921)


In recent years, there are various improvements in computational image processing methods to assist pathologists in detecting cancer cells. Consequently, deep learning algorithm known as Convolutional Neural Network (CNN) has now become a popular method in the application image detection and analysis using histopathology image (images of tissues and cells). This study presents the histopathology image related to breast cancer cells detection (mitosis and non-mitosis). Mitosis is an important parameter for the prognosis/diagnosis of breast cancer. However, mitosis detection in histopathology image is a challenging problem that needs a deeper investigation. This is because mitosis consists of small objects with a variety of shapes, and is easily confused with some other objects or artefacts present in the image. Hence, this study proposed three types of deep layer CNN architecture which are called 6-layer CNN, 13-layer CNN and 17-layer CNN, respectively in detecting breast cancer cells using histopathology image. The aim of this study is to detect the breast cancer cell which is called mitosis from histopathology image using suitable layer in deep layer CNN with the highest accuracy and True Positive Rate (TPR), and the lowest False Positive Rate (FPR) and loss performances. The result shows a promising performance for deep layer CNN architecture of 17-layer CNN is suitable for this dataset with the highest average accuracy, 84.49% and True Positive Rate (TPR), 80.55%; while the least False Positive Rate (FNR), 11.66% and loss 15.50%.


Breast cancer image detection Deep Learning Histopathology image Convolutional Neural Network (CNN) 



This work is supported by Ministry of Education (MOE), Malaysia, Universiti Teknologi Malaysia (UTM), Malaysia and ASEAN-Indian Research Grant. This paper is financially supported by MYBRAIN, Grant No. 17H62, 03G91, and 04G48. The authors would like to express their deepest gratitude to the Bram van Ginneken, SjoerdKerkstra, and James Meakin for their support in providing the MITOS-ATYPHIA datasets to ensure the success of this research.


  1. 1.
    Howlader, C.K., Noone, N., Krapcho, M., Garshell, J., Miller, D., Altekruse, S.F., Kosary, C.L., Yu, M., Ruhl, J., Tatalovich, Z., Mariotto, A., Lewis, D.R., Chen, H.S., Feuer, E.J., Cancer statistics review 1975-2012: introduction, pp. 1–101 (2015)Google Scholar
  2. 2.
    Siegel, R., Naishadham, D., Jemal, A., Ma, J., Zou, Z., Jemal, A.: Cancer statistics, 2014. CA Cancer J. Clin. 64(1), 9–29 (2014)CrossRefGoogle Scholar
  3. 3.
    Veta, M., et al.: Assessment of algorithms for mitosis detection in breast cancer histopathology images. Med. Image Anal. 20(1), 237–248 (2015)CrossRefGoogle Scholar
  4. 4.
    Zhang, S., Grave, E., Sklar, E., Elhadad, N.: Longitudinal analysis of discussion topics in an online breast cancer community using convolutional neural networks. J. Biomed. Inform. 69, 1–9 (2017)CrossRefGoogle Scholar
  5. 5.
    Dalle, J.-R., Leow, W.K., Racoceanu, D., Tutac, A.E., Putti, T.C.: Automatic breast cancer grading of histopathological images. In: The 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 20–25 August 2008, Vancouver, BC, Canada (2008)Google Scholar
  6. 6.
    Mohd, A., Khuzi, R., Besar, W.M.D., Zaki, W., Ahmad, N.N.: Identification of masses in digital mammogram using gray level co-occurrence matrices. Biomed. Imaging Interv. J. 5, 1–13 (2009)Google Scholar
  7. 7.
    Singh, S., Gupta, P., Sharma, M.: Breast cancer detection and classification of histopathological images. Int. J. Eng. 3(5), 4228–4232 (2010)Google Scholar
  8. 8.
    Lim, G.C.C., Halimah, Y.: Cancer incidence in Peninsular Malaysia 2003-2005. National Cancer Registry (2008)Google Scholar
  9. 9.
    Khan, A.M., Rajpoot, N., Treanor, D., Magee, D.: A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution. IEEE Trans. Biomed. Eng. 61(6), 1729–1738 (2014)CrossRefGoogle Scholar
  10. 10.
    Kothari, S., Phan, J., Wang, M.: Eliminating tissue-fold artifacts in histopathological whole-slide images for improved image-based prediction of cancer grade. J. Pathol. Inform. 4, 22 (2013)CrossRefGoogle Scholar
  11. 11.
    Veta, M., van Diest, P.J., Kornegoor, R., Huisman, A., Viergever, M.A., Pluim, J.P.W.: Automatic nuclei segmentation in H&E stained breast cancer histopathology images. PLoS ONE 8, e70221 (2013)CrossRefGoogle Scholar
  12. 12.
    Shen, W., et al.: Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification. Pattern Recogn. 61, 663–673 (2017)CrossRefGoogle Scholar
  13. 13.
    Ciresan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Mitosis detection in breast cancer histology images with deep neural networks. In: Lecture Notes Computer Science (including Subseries Lecture Notes Artificial Intelligent Lecture Notes Bioinformatics), vol. 8150, LNCS, PART 2, pp. 411–418 (2013)Google Scholar
  14. 14.
    Su, H., Liu, F., Xie, Y., Xing, F., Meyyappan, S., Yang, L.: Region segmentation in histopathological breast cancer images using deep convolutional neural network. In: 2015 IEEE 12th International Symposium on Biomedical Imaging, pp. 55–58 (2015)Google Scholar
  15. 15.
    Wahlstr, N.: Learning deep dynamical models from image pixels (2016)Google Scholar
  16. 16.
    Feng, Y., Zhang, L., Yi, Z.: Breast cancer cell nuclei classification in histopathology images using deep neural networks. Int. J. Comput. Assist. Radiol. Surg. 13(2), 179–191 (2018)CrossRefGoogle Scholar
  17. 17.
    Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: Breast cancer histopathological image classification using convolutional neural networks. In: 2016 International Joint Conference on Neural Networks, pp. 2560–2567 (2016)Google Scholar
  18. 18.
    Roux, L., et al.: Mitosis detection in breast cancer histological images an ICPR 2012 contest. J. Pathol. Inform. 4(1), 8 (2013)CrossRefGoogle Scholar
  19. 19.
    Veta, M., Pluim, J.P.W., van Diest, P.J., Viergever, M.A.: Breast cancer histopathology image analysis: a review 61, 1400–1411 (2014)Google Scholar
  20. 20.
    Kotzias, D.: From Group to Individual Labels using Deep Features (2015)Google Scholar
  21. 21.
    Wu, S., Zhong, S., Liu, Y.: Deep residual learning for image steganalysis. Multimed. Tools Appl. 77, 1–17 (2017)Google Scholar
  22. 22.
    Wahab, N., Khan, A., Lee, Y.S.: Two-phase deep convolutional neural network for reducing class skewness in histopathological images based breast cancer detection. Comput. Biol. Med. 85(April), 86–97 (2017)CrossRefGoogle Scholar
  23. 23.
    Demir, C., Yener, B.: Automated cancer diagnosis based on histopathological images: a systematic survey. Technical Report vol. TR-05-09, pp. 1–16, Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, USA (2005)Google Scholar
  24. 24.
    Bhattacharjee, S., Mukherjee, J., Nag, S., Maitra, I.K., Bandyopadhyay, S.K.: Review on histopathological slide analysis using digital microscopy. Int. J. Adv. Sci. Technol. 62, 65–96 (2014)CrossRefGoogle Scholar
  25. 25.
    Hassanien, A.E., Ali, J.M., Nobuhara, H.: Detection of spiculated masses in Mammograms based on fuzzy image processing, In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) Artificial Intelligence and Soft Computing - ICAISC 2004. Lecture Notes in Computer Science, vol. 3070, pp. 102–107. Springer, Berlin, Heidelberg (2004)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Zanariah Zainudin
    • 1
    Email author
  • Siti Mariyam Shamsuddin
    • 1
  • Shafaatunnur Hasan
    • 1
  1. 1.School of Computing, Faculty of EngineeringUniversiti Teknologi MalaysiaSkudaiMalaysia

Personalised recommendations