Detection of Breast Cancer Using Infrared Thermography and Deep Neural Networks

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11466)


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 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.


  1. 1.
    Anderson, B.O., et al.: Breast J. 12(Suppl 1:S3-15) (2005). PMID: 16430397Google Scholar
  2. 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. 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. 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. 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. 6.
    Silva, L.F., et al.: A new database for breast research with infrared image. Banco de imágenes Visual Lab (2014).
  7. 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. 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. 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. 10.
    Guerrero, S.R., Loaiza, H., Retrepo, A.D.: Automatic segmentation of thermal images to support breast cáncer diagnosis (2014)Google Scholar
  11. 11.
    Kandlikar, S.G., et al.: Infrared imaging technology for breast cancer detection – Current status, protocols and new directions (2017)CrossRefGoogle Scholar
  12. 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)CrossRefGoogle Scholar
  13. 13.
    Smith, L.N.: Cyclical learning rates for training neuronal networks (2014)Google Scholar
  14. 14.
    Takahashi, R., Matsubara, T., Uehara, K.: Data Augmentation using Random Image Cropping and Patching for Deep CNNs (2018)Google Scholar
  15. 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. 16.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition (2015)Google Scholar
  17. 17.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014)Google Scholar
  18. 18.
    Fernández-Muñiz, Z., Khaniani, H., Fernández-Martínez, J.L.: Data kit inversion and uncertainty analysis. J. Appl. Geophys. 161, 228–238 (2019)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Group of Inverse Problems, Optimization and Machine Learning, Department of MathematicsUniversity of OviedoOviedoSpain
  2. 2.Department of MathematicsTechnical University of CataloniaBarcelonaSpain
  3. 3.Department of Informatics and Computer ScienceUniversity of OviedoOviedoSpain

Personalised recommendations