Skip to main content

Classification Of Breast Cancer Histology Images Using ALEXNET

  • Conference paper
  • First Online:
Image Analysis and Recognition (ICIAR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10882))

Included in the following conference series:

Abstract

Training a deep convolutional neural network from scratch requires massive amount of data and significant computational power. However, to collect a large amount of data in medical field is costly and difficult, but this can be solved by some clever tricks such as mirroring, rotating and fine tuning pre-trained neural networks. In this paper, we fine tune a deep convolutional neural network (ALEXNET) by changing and inserting input layer convolutional layers and fully connected layer. Experimental results show that our method achieves a patch and image-wise accuracy of 75.73% and 81.25% respectively on the validation set and image-wise accuracy of 57% on the ICIAR-2018 breast cancer challenge hidden test set.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2015. CA Cancer J. Clin. 65(1), 5–29 (2015)

    Article  Google Scholar 

  2. National Breast Cancer Foundation. Breast Cancer Diagnosis (2015). http://www.nationalbreastcancer.org/breast-cancer-diagnosis

  3. Elston, C.W., Ellis, I.O.: Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. Histopathology. 19(5), 403–10 (1991)

    Article  Google Scholar 

  4. Filipczuk, P., Fevens, T., Krzyzak, A., Monczak, R.: Computer-aided breast cancer diagnosis based on the analysis of cytological images of fine needle biopsies. IEEE Trans. Med. Imag. 32(12), 2169–2178 (2013)

    Article  Google Scholar 

  5. George, Y.M., Zayed, H.H., Roushdy, M.I., Elbagoury, B.M.: Remote computer-aided breast cancer detection and diagnosis system based on cytological images. IEEE Syst. J. 8(3), 949–64 (2014)

    Article  Google Scholar 

  6. Belsare, A.D., Mushrif, M.M., Pangarkar, M.A., Meshram, N.: Classification of breast cancer histopathology images using texture feature analysis. In: 2015 IEEE Region 10 Conference, TENCON 2015, Macau, p. 1–5. IEEE (2015)

    Google Scholar 

  7. Brook, A., El-Yaniv, R., Issler, E., Kimmel, R., Meir, R., Peleg, D.: Breast cancer diagnosis from biopsy images using generic features and SVMs, pp. 1–16 (2007)

    Google Scholar 

  8. Zhang, B.: Breast cancer diagnosis from biopsy images by serial fusion of Random Subspace ensembles. In: 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI), vol. 1. IEEE (2011)

    Google Scholar 

  9. Israel Institute of Technology dataset. ftp.cs.technion.ac.il/pub/projects/medic-image

    Google Scholar 

  10. Krizhevsky, A., Ilya S., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (2012)

    Google Scholar 

  11. Szegedy, C.: Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition (2015)

    Google Scholar 

  12. Cireşan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Mitosis detection in breast cancer histology images with deep neural networks. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 411–418. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40763-5_51

    Chapter  Google Scholar 

  13. Litjens, G.: Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci. Rep. 6, 26286 (2016)

    Article  Google Scholar 

  14. Sirinukunwattana, K.: Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans. Med. Imag. 35(5), 1196–1206 (2016)

    Article  Google Scholar 

  15. Chi, J.: Thyroid nodule classification in ultrasound images by fine-tuning deep convolutional neural network. J. Digit. Imag. 30(4), 477–486 (2017)

    Article  Google Scholar 

  16. Cruz-Roa, A.: Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks. In: International Society for Optics and Photonics, SPIE Medical Imaging, vol. 9041 (2014)

    Google Scholar 

  17. Araújo, T.: Classification of breast cancer histology images using Convolutional Neural Networks. PloS one 12(6), e0177544 (2017)

    Article  Google Scholar 

  18. Bayramoglu, N., Heikkilä, J.: Transfer learning for cell nuclei classification in histopathology images. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 532–539. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49409-8_46

    Chapter  Google Scholar 

  19. Macenko, M.: A method for normalizing histology slides for quantitative analysis. In: 2009 IEEE International Symposium on Biomedical Imaging, ISBI2009, From Nano to Macro. IEEE (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wajahat Nawaz .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Nawaz, W., Ahmed, S., Tahir, A., Khan, H.A. (2018). Classification Of Breast Cancer Histology Images Using ALEXNET. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science(), vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_99

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-93000-8_99

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92999-6

  • Online ISBN: 978-3-319-93000-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics