Abstract
Breast cancer is one of the prevalent types of cancer. Early diagnosis and treatment of breast cancer have vital importance for patients. Various imaging techniques are used in the detection of cancer. Thermal images are obtained by using the temperature difference of regions without giving radiation by the thermal camera. In this study, we present methods for computer aided diagnosis of breast cancer using thermal images. To this end, various Convolutional Neural Networks (CNNs) have been designed by using transfer learning methodology. The performance of the designed nets was evaluated on a benchmarking dataset considering accuracy, precision, recall, F1 measure, and Matthews Correlation coefficient. The results show that an architecture holding pre-trained convolutional layers and training newly added fully connected layers achieves a better performance compared with others. We have obtained an accuracy of 94.3%, a precision of 94.7% and a recall of 93.3% using transfer learning methodology with CNN.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
World Health Organization Homepage. http://www.who.int/mediacentre/factsheets/fs297/en/. Accessed 21 Mar 2019
Nyström, L., et al.: Breast cancer screening with mammography: overview of Swedish randomised trials. The Lancet. 341, 973–978 (1993)
Griffiths, C., Brock, A.: Twentieth century mortality trends in England and Wales. Health Stat Q. 18, 5–17 (2003)
Kandlikar, S., et al.: Infrared imaging technology for breast cancer detection – current status, protocols and new directions. Int. J. Heat Mass Transf. 108, 2303–2320 (2017)
Saslow, D., et al.: American cancer society guidelines for breast screening with MRI as an adjunct to mammography. CA Cancer J. Clin. 57, 75–89 (2007)
Baffa, M., Lattari, L.: Convolutional neural networks for static and dynamic breast infrared imaging classification. In: 31st Conference on Graphics, Patterns and Images (SIBGRAPI), Brazil, pp. 174–181. IEEE (2018)
Faust, O., Rajendra Acharya, U., Ng, E., Hong, T., Yu, W.: Application of infrared thermography in computer aided diagnosis. Infrared Phys. Technol. 66, 160–175 (2014)
Oğul, H., Oğul, B., Ağıldere, A., Bayrak, T., Sümer, E.: Eliminating rib shadows in chest radiographic images providing diagnostic assistance. Comput. Methods Programs Biomed. 127, 174–184 (2016)
Silva, L., et al.: A new database for breast research with infrared image. J. Med. Imaging Health Inf. 4, 92–100 (2014)
Araújo, M., Lima, R., de Souza, R.: Interval symbolic feature extraction for thermography breast cancer detection. Expert Syst. Appl. 41, 6728–6737 (2014)
Krawczyk, B., Schaefer, G.: Breast thermogram analysis using classifier ensembles and image symmetry features. IEEE Syst. J. 8, 921–928 (2014)
Gaber, T., et al.: Thermogram breast cancer prediction approach based on neutrosophic sets and fuzzy C-means algorithm. In: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4254–4257 (2015)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Ji, S., Xu, W., Yang, M., Yu, K.: 3D convolutional neural networks for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35, 221–231 (2013)
Pan, S., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22, 1345–1359 (2010)
Lu, J., Behbood, V., Hao, P., Zuo, H., Xue, S., Zhang, G.: Transfer learning using computational intelligence: a survey. Knowl. Based Syst. 80, 14–23 (2015)
Gogoi, U.R., Majumdar, G., Bhowmik, M.K., Ghosh, A.K., Bhattacharjee, D.: Breast abnormality detection through statistical feature analysis using infrared thermograms. In: 2015 Fifth International Conference on Advanced Computing & Communication Technologies (ISACC), India, pp. 258–265. IEEE (2015)
Silva, L.F., et al.: A new database for breast research with infrared image. J. Med. Imaging Health Inf. 4(1), 92–100 (2014)
Lessa, V., Marengoni, M.: Applying artificial neural network for the classification of breast cancer using infrared thermographic images. In: Chmielewski, L.J., Datta, A., Kozera, R., Wojciechowski, K. (eds.) ICCVG 2016. LNCS, vol. 9972, pp. 429–438. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46418-3_38
Borchartt, T., et al.: Combining approaches for early diagnosis of breast diseases using thermal imaging. Int. J. Innovative Comput. Appl. 4, 163 (2012)
Sathish, D., Kamath, S., Prasad, K., Kadavigere, R., Martis, R.: Asymmetry analysis of breast thermograms using automated segmentation and texture features. SIViP 11, 745–752 (2016). https://doi.org/10.1007/s11760-016-1018-y
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Cabıoğlu, Ç., Oğul, H. (2020). Computer-Aided Breast Cancer Diagnosis from Thermal Images Using Transfer Learning. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2020. Lecture Notes in Computer Science(), vol 12108. Springer, Cham. https://doi.org/10.1007/978-3-030-45385-5_64
Download citation
DOI: https://doi.org/10.1007/978-3-030-45385-5_64
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-45384-8
Online ISBN: 978-3-030-45385-5
eBook Packages: Computer ScienceComputer Science (R0)