Abstract
Early-stage breast cancer detection is often thwarted due to privacy concerns, the need for regular scanning, among other factors, thereby severely reducing the survival rate of patients. Thermography is an emerging low cost, portable, non-invasive, and privacy-sensitive technique for early-stage breast cancer detection gaining popularity over the traditional mammography based technique that requires expert intervention in a lab setup. Earlier proposals for machine learning augmented thermography for early-stage breast cancer detection suffer from precision as well as performance challenges. We developed a novel voting based machine learning model with on the fly parallel retraining using the Dask library. Experimental evaluation reveals that our novel high-performance thermography based learning technique brings up the accuracy of early-stage life-saving breast cancer detection to 93%.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
National Breast Cancer; Breast Cancer Diagnosis. https://www.nationalbreastcancer.org/breast-cancer-diagnosis/, accessed 2020-03.
- 2.
The DMR-IR dataset used in this work is publicly available at http://visual.ic.uff.br/dmi.
References
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(3), 1503–1510 (2012)
Arena, F., Barone, C., DiCicco, T.M.: Use of digital infrared imaging in enhanced breast cancer detection and monitoring of the clinical response to treatment. In: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No. 03CH37439), vol. 2, pp. 1129–1132 (2003)
Kandlikar, S.G., et al.: Infrared imaging technology for breast cancer detection-current status, protocols and new directions. Int. J. Heat Mass Transf. 108, 2303–2320 (2017)
Krawczyk, B., Schaefer, G., Zhu, S.Y.: Breast cancer identification based on thermal analysis and a clustering and selection classification ensemble. In: Imamura, K., Usui, S., Shirao, T., Kasamatsu, T., Schwabe, L., Zhong, N. (eds.) BHI 2013. LNCS (LNAI), vol. 8211, pp. 256–265. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-02753-1_26
Lorenzo, P.R.: Particle swarm optimization for hyper-parameter selection in deep neural networks. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 481–488. ACM (2017)
Mambou, S.J., Maresova, P., Krejcar, O., Selamat, A., Kuca, K.: Breast cancer detection using infrared thermal imaging and a deep learning model. Sensors 18(9), 2799 (2018)
Ng, E.K.: A review of thermography as promising non-invasive detection modality for breast tumor. Int. J. Therm. Sci. 48(5), 849–859 (2009)
Niramai: Thermalytix. https://niramai.com/
Schaefer, G., Závišek, M., Nakashima, T.: Thermography based breast cancer analysis using statistical features and fuzzy classification. Pattern Recogn. 42, 1133–1137 (2009). https://doi.org/10.1016/j.patcog.2008.08.007
Zuluaga-Gomez, J., Zerhouni, N., Al Masry, Z., Devalland, C., Varnier, C.: A survey of breast cancer screening techniques: thermography and electrical impedance tomography. J. Med. Eng. Technol. 43(5), 305–322 (2019)
Zuluaga-Gomez, J., Masry, Z.A., Benaggoune, K., Meraghni, S., Zerhouni, N.: A CNN-based methodology for breast cancer diagnosis using thermal images (2019)
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
Rajashekar, V., Lagwankar, I., S N, D.P., Nagpal, R. (2020). Efficient Thermography Guided Learning for Breast Cancer Detection. In: Qiu, M. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2020. Lecture Notes in Computer Science(), vol 12454. Springer, Cham. https://doi.org/10.1007/978-3-030-60248-2_40
Download citation
DOI: https://doi.org/10.1007/978-3-030-60248-2_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60247-5
Online ISBN: 978-3-030-60248-2
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)