Skip to main content
SpringerLink
Log in

Applying Artificial Neural Network for the Classification of Breast Cancer Using Infrared Thermographic Images

  • Conference paper
  • First Online:
Computer Vision and Graphics (ICCVG 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9972))

Included in the following conference series:

Abstract

The second type of cancer that kills more women in the world is breast cancer. If the prognosis is done at an early stage of the disease, women can have a better chance of cure. However, the access to medical exams in poor countries is usually precarious. This work describes the study of a computer-assisted diagnostic system using thermal imaging. The images are generated by a thermographic camera that has a lower cost than the equipment used in conventional exams. We propose a system that classifies the thermographic breasts images in “normal” and “abnormal”. We have analyzed 8 statistical characteristics: mean, variance, standard deviation (SD), skewness, kurtosis, entropy, range and median. The classification used an Artificial Neural Network (ANN) and got a result of 87 % in sensitivity, 83 % in specificity and 85 % in accuracy.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

References

  1. Ferlay, J., Soerjomataram, M., Ervik, M., Dikshit, R., Eser, S., Mathers, C., Rebelo, M., Parkin, D., Forman, D., Bray, F.: Cancer Incidence and Mortality Worldwide: IARC Cancer Base n 11 (2012). http://globocan.iarc.fr

  2. Ng, E., Sudharsan, N.: Numerical computation as a tool to aid thermographic interpretation. J. Med. Eng. Technol. 25, 53–60 (2001)

    Article  Google Scholar 

  3. Borchartt, T.: Thermographic Image Analysis for the Change of Classification in Breast. Univerisadade Federal Fluminense, Brasil (2013)

    Google Scholar 

  4. Thomsen, L., Miles, D.: Happerfield: nitrie oxide synthase activity in human breast. Br. J. Cancer 72, 41 (1995)

    Article  Google Scholar 

  5. Silva, L.F., Saade, D.C.M., Sequeiros, G.O., Silva, A.C., Paiva, A.C., Bravo, R.S., Conci, A.: A new database for breast research with infrared image. J. Med. Imaging Health Inf. 4(1), 92–100 (2014)

    Article  Google Scholar 

  6. Canny, J.: A computational approach to edge detectiion. IEEE Trans. Pattern Anal. Mach. Intell. 6, 679–698 (1995)

    Google Scholar 

  7. Nurhayati, O., Susanto, A., Sri Widodo, T., Tjokronagoro, M.: Principal component analysis combined with first order statistical method for breast thermal images classification. Int. J. Comput. Sci. Technol. (JCST) 2(2), 12–18 (2011)

    Google Scholar 

  8. Freeman, J.A., Skapura, D.M.: Neural Networks: Algorithms, Applications, and Programming Techniques. Addison-Wesley, New York (1992)

    MATH  Google Scholar 

  9. Zhou, Q., Li, Z., Aggarwal, J.K.: Boundary extraction in thermal images by edge map. In: Proceedings of the ACM Symposium on Applied Computing, pp. 254–258 (2004)

    Google Scholar 

  10. Haykin, S.: Neural networks: principles and practice. Trad. Paulo Martins Engel, Porto Alegre, Bookman, 2nd edn., p. 893 (2008)

    Google Scholar 

  11. Koay, J., Herry, C., Frize, M.: Analysis of breast thermography with an artificial neural network. In: Conference of Proceedings IEEE Engineering in Medicine and Biology Society, vol. 2, p. 1159 (2004)

    Google Scholar 

  12. Tang, X., Ding, H., Yuan, Y., Wang, Q.: Morphological measurement of localized temperature increase amplitudes in breast infrared thermograms and its clinical application. In: Biomedical Signal Processing and Control, vol. 3(4), p. 312, October 2008

    Google Scholar 

  13. Arora, N., Martins, D., Ruggerio, D., Tousimis, E., Swistel, A., Osborne, M., Simmons, R.: Effectiveness of a noninvasive digital infrared thermal imaging system in the detection of breast cancer. Am. J. Surg. 196, 523–526 (2008)

    Article  Google Scholar 

  14. Wishart, G.C., Campisi, M., Boswell, M., Chapman, D., Shackleton, V., Iddles, S., Hallett, A., Britton, P.D.: The accuracy of digital infrared imaging for breast cancer detection in women undergoing breast biopsy. Euro. J. Surg. Oncol. (EJSO) 36, 535–540 (2010)

    Article  Google Scholar 

  15. Umadevi, V., Raghavan, S.V., Jaipurkar, S.: Interpreter for breast thermogram characterization. In: Conference on Biomedical Engineering and Sciences (IECBES), Kuala Lumpur, p. 150 (2010)

    Google Scholar 

  16. Acharya, U.R., Ng, E.Y.K., Tan, J.H., Sree, S.V.: Thermography based breast cancer detection using texture features and support vector machine. J. Med. Syst. 36, 1503–1510 (2012)

    Article  Google Scholar 

  17. Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Presbiterian University Mackenzie, São Paulo, 01302-907, Brazil

    Vanessa Lessa & Mauricio Marengoni

Authors
  1. Vanessa Lessa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mauricio Marengoni
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vanessa Lessa .

Editor information

Editors and Affiliations

  1. Faculty of Applied Informatics and Math, Warsaw University of Life Sciences , Warsaw, Poland

    Leszek J. Chmielewski

  2. Computer Sci and Software Engineering, University of Western Australia , Perth, Australia

    Amitava Datta

  3. Faculty of Applied Informatics and Math, Warsaw University of Life Sciences SGGW , Warsaw, Poland

    Ryszard Kozera

  4. Institute of Computer Science, Silesian University of Technology , Gliwice, Poland

    Konrad Wojciechowski

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Lessa, V., Marengoni, M. (2016). Applying Artificial Neural Network for the Classification of Breast Cancer Using Infrared Thermographic Images. In: Chmielewski, L., Datta, A., Kozera, R., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2016. Lecture Notes in Computer Science(), vol 9972. Springer, Cham. https://doi.org/10.1007/978-3-319-46418-3_38

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-46418-3_38

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46417-6

  • Online ISBN: 978-3-319-46418-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation