Skip to main content

Detection of Breast Cancer Using Infrared Thermography and Deep Neural Networks

Part of the Lecture Notes in Computer Science book series (LNBI,volume 11466)

Abstract

We present a preliminary analysis about the use of convolutional neural networks (CNNs) for the early detection of breast cancer via infrared thermography. The two main challenges of using CNNs are having at disposal a large set of images and the required processing time. The thermographies were obtained from Vision Lab and the calculations were implemented using Fast.ai and Pytorch libraries, which offer excellent results in image classification. Different architectures of convolutional neural networks were compared and the best results were obtained with resnet34 and resnet50, reaching a predictive accuracy of 100% in blind validation. Other arquitectures also provided high classification accuracies. Deep neural networks provide excellent results in the early detection of breast cancer via infrared thermographies, with technical and computational resources that can be easily implemented in medical practice. Further research is needed to asses the probabilistic localization of the tumor regions using larger sets of annotated images and assessing the uncertainty of these techniques in the diagnosis.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-17935-9_46
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   79.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-17935-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   99.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.

References

  1. Anderson, B.O., et al.: Breast J. 12(Suppl 1:S3-15) (2005). PMID: 16430397

    Google Scholar 

  2. Pérez, M.G., Conci, A., Aguilar, A., Sánchez, A., Andaluz, V.H.: Detección temprana del cáncer de mama mediante la termografía en Ecuador (2014)

    Google Scholar 

  3. Araújo, M.C., Lima, R.C.F., de Souza, R.M.C.R.: Interval symbolic feature extraction for thermography breast cancer detection (2014)

    Google Scholar 

  4. Gogoi, U.R., Majumdar, G., Bhowmik, M.K., Ghosh, A.K., Bhattacharjee, D.: Breast abnormality detection through statistical feature analysis using infrared thermograms (2015)

    Google Scholar 

  5. Mejía, T.M., Pérez, M.G., Andaluz, V.H., Conci, A.: Automatic segmentation and analysis of thermograms using texture descriptors for breast cancer detection (2015)

    Google Scholar 

  6. Silva, L.F., et al.: A new database for breast research with infrared image. Banco de imágenes Visual Lab (2014). http://visual.ic.uff.br/dmi

  7. 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 (2012)

    Google Scholar 

  8. Ali, M.A.S., Hassanien, A.E., Gaber, T., Silva, L.: Detection of breast abnormalities of thermograms based on a new segmentation method (2015)

    Google Scholar 

  9. Sathish, D., Kamath, S., Prasad, K., Kadavigere, R., Martis, R.J.: Asymmetry analysis of breast thermograms using automated segmentation and texture features (2016)

    Google Scholar 

  10. Guerrero, S.R., Loaiza, H., Retrepo, A.D.: Automatic segmentation of thermal images to support breast cáncer diagnosis (2014)

    Google Scholar 

  11. Kandlikar, S.G., et al.: Infrared imaging technology for breast cancer detection – Current status, protocols and new directions (2017)

    CrossRef  Google Scholar 

  12. Fernández‐Martínez, J.L., Xu, S., Sirieix, C., Fernández‐Muniz, Z., Riss, J.: Uncertainty analysis and probabilistic segmentation of electrical resistivity images: the 2D inverse problem. Geophys. Prospect. 65, 112–130 (2017)

    CrossRef  Google Scholar 

  13. Smith, L.N.: Cyclical learning rates for training neuronal networks (2014)

    Google Scholar 

  14. Takahashi, R., Matsubara, T., Uehara, K.: Data Augmentation using Random Image Cropping and Patching for Deep CNNs (2018)

    Google Scholar 

  15. Montone, G., O’Regan, J.K., Terekhov, A.V.: Gradual Tuning: a better way of Fine Tuning the parameters of a Deep Neural Network (2017)

    Google Scholar 

  16. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition (2015)

    Google Scholar 

  17. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014)

    Google Scholar 

  18. Fernández-Muñiz, Z., Khaniani, H., Fernández-Martínez, J.L.: Data kit inversion and uncertainty analysis. J. Appl. Geophys. 161, 228–238 (2019)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Edwin Santiago Alférez-Baquero or Juan Luis Fernández-Martínez .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Fernández-Ovies, F.J., Santiago Alférez-Baquero, E., de Andrés-Galiana, E.J., Cernea, A., Fernández-Muñiz, Z., Fernández-Martínez, J.L. (2019). Detection of Breast Cancer Using Infrared Thermography and Deep Neural Networks. In: Rojas, I., Valenzuela, O., Rojas, F., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2019. Lecture Notes in Computer Science(), vol 11466. Springer, Cham. https://doi.org/10.1007/978-3-030-17935-9_46

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-17935-9_46

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-17934-2

  • Online ISBN: 978-3-030-17935-9

  • eBook Packages: Computer ScienceComputer Science (R0)