Skip to main content
SpringerLink
Log in

A Novel Thermography-Based Artificial Intelligence-Powered Solution for Screening Breast Cancer

  • Conference paper
  • First Online:
Artificial Intelligence over Infrared Images for Medical Applications and Medical Image Assisted Biomarker Discovery (MIABID 2022, AIIIMA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13602))

  • 273 Accesses

Abstract

Breast cancer is one of the most common cancers in women and 7% of breast cancer cases in the United States occur in women under the age of 40. Early diagnosis and intervention are essential not only for better patient prognosis, but also to reduce the ever-increasing burden on the healthcare system across the globe. The current gold standard for breast cancer diagnosis, mammography, is limited in its capacity to detect breast cancer in early stages especially with younger women with dense breast tissue. Additionally, there is limited accessibility and affordability of mammography in low- and middle-income countries. Thermography, on the other hand, can detect cancer in very early stages and in this paper we discuss an AI-powered thermography-based breast cancer prediction tool. The proposed method involves data pre-processing, data augmentation, a detailed training strategy, and a post-processing risk calculation step. The proposed algorithm was trained using 1600 images from breast thermography databases to detect abnormalities in the breast tissue. On our dataset, we obtained an accuracy of 93%, 95% precision with >90% specificity and sensitivity, which is a significant breakthrough in using thermography as a potential screening for breast cancer. Additionally, with the risk calculator, the model can predict the risk of developing breast cancer in the future. The high accuracy of our proposed model and the risk-prediction capabilities enable the AI-powered screening tool by AI Talos to become the computer-aided diagnostic system that supports screening and early detection of breast cancer especially in younger population.

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 44.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 59.99
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. Breast cancer (2021). https://www.who.int/news-room/fact-sheets/detail/breast-cancer

  2. https://www.cancer.org/cancer/breast-cancer/about/how-common-is-breast-cancer.html

  3. Miao, H., et al.: Incidence and outcome of male breast cancer: an international population-based study. J. Clin. Oncol. 29, 4381–4386 (2011)

    Article  Google Scholar 

  4. Unger-Saldaña, K.: Challenges to the early diagnosis and treatment of breast cancer in developing countries. World J. Clin. Oncol. 5, 465–477 (2014)

    Article  Google Scholar 

  5. Anderson, B.O., Jakesz, R.: Breast cancer issues in developing countries: an overview of the breast health global initiative. World J. Surg. 32, 2578–2585 (2008)

    Article  Google Scholar 

  6. Anders, C.K., Johnson, R., Litton, J., Phillips, M., Bleyer, A.: Breast cancer before age 40 years. Semin. Oncol. 36, 237–249 (2009)

    Article  Google Scholar 

  7. Oeffinger, K.C., et al.: Breast cancer screening for women at average risk: 2015 guideline update from the American cancer society. JAMA 314, 1599 (2015)

    Article  Google Scholar 

  8. Siu, A.L., Preventive Services Task Force: Screening for breast cancer: U.S. preventive services task force recommendation statement. Ann. Intern. Med. 164, 279 (2016)

    Google Scholar 

  9. Migowski, A.: A detecção precoce do câncer de mama e a interpretação dos resultados de estudos de sobrevida. Ciênc. Saúde Coletiva 20, 1309 (2015)

    Article  Google Scholar 

  10. Reeves, R.A., Kaufman, T.: Mammography. In: StatPearls. StatPearls Publishing (2022)

    Google Scholar 

  11. Li, T.: The association of measured breast tissue characteristics with mammographic density and other risk factors for breast cancer. Cancer Epidemiol. Biomarkers Prev. 14, 343–349 (2005)

    Article  Google Scholar 

  12. McCormack, V.A.: Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol. Biomarkers Prev. 15, 1159–1169 (2006)

    Article  Google Scholar 

  13. Breast Cancer Screening: Thermogram No Substitute for Mammogram (2021). https://www.fda.gov/consumers/consumer-updates/breast-cancer-screening-thermogram-no-substitute-mammogram

  14. Kakileti, S.T., Manjunath, G., Madhu, H., Ramprakash, H.V.: Advances in breast thermography. In: Malik, A.M. (ed.) New Perspectives in Breast Imaging. InTech (2017). https://doi.org/10.5772/intechopen.69198

    Chapter  Google Scholar 

  15. Quinn, T.J., Compton, J.P.: The foundations of thermometry. Rep. Prog. Phys. 38, 151–239 (1975)

    Article  Google Scholar 

  16. Anderson, R.R., Parrish, J.A.: The optics of human skin. J. Invest. Dermatol. 77, 13–19 (1981)

    Article  Google Scholar 

  17. Hardy, J.D.: The radiation of heat from the human body. J. Clin. Invest. 13, 615–620 (1934)

    Article  Google Scholar 

  18. Hardy, J.D., Hammel, H.T., Murgatroyd, D.: Spectral transmittance and reflectance of excised human skin. J. Appl. Physiol. 9, 257–264 (1956)

    Article  Google Scholar 

  19. Pratt, W.K.: Introduction to digital image processing (2014)

    Google Scholar 

  20. Clark, C., Vinegar, R., Hardy, J.D.: Goniometric spectrometer for the measurement of diffuse reflectance and transmittance of skin in the infrared spectral region. J. Opt. Soc. Am. 43, 993 (1953)

    Article  Google Scholar 

  21. Lawson, R.: Implications of surface temperatures in the diagnosis of breast cancer. Can. Med. Assoc. J. 75, 309–311 (1956)

    Google Scholar 

  22. Li, C.Y., et al.: Initial stages of tumor cell-induced angiogenesis: evaluation via skin window chambers in rodent models. J. Natl. Cancer Inst. 92, 143–147 (2000)

    Article  Google Scholar 

  23. Gautherie, M.: Thermobiological assessment of benign and malignant breast diseases. Am. J. Obstet. Gynecol. 147, 861–869 (1983)

    Article  Google Scholar 

  24. Ng, E.Y.-K.: A review of thermography as promising non-invasive detection modality for breast tumor. Int. J. Therm. Sci. 48, 849–859 (2009)

    Article  Google Scholar 

  25. Yoshida, S., Nakagawa, S., Yahara, T., Koga, T., Deguchi, H., Shirouzu, K.: Relationship between microvessel density and thermographic hot areas in breast cancer. Surgery Today 33(4), 243–248 (2003). https://doi.org/10.1007/s005950300055

    Article  Google Scholar 

  26. Ng, E.Y.K., Sudharsan, N.M.: An improved three-dimensional direct numerical modelling and thermal analysis of a female breast with tumour. Proc. Inst. Mech. Eng. [H] 215, 25–37 (2001)

    Article  Google Scholar 

  27. Head, J.F., Wang, F., Elliott, R.L.: Breast thermography is a noninvasive prognostic procedure that predicts tumor growth rate in breast cancer patients. Ann. N. Y. Acad. Sci. 698, 153–158 (1993)

    Article  Google Scholar 

  28. Gautherie, M., Gros, C.M.: Breast thermography and cancer risk prediction. Cancer 45, 51–56 (1980)

    Article  Google Scholar 

  29. Amalric, R., et al.: Does infrared thermography truly have a role in present-day breast cancer management? Prog. Clin. Biol. Res. 107, 269–278 (1982)

    Google Scholar 

  30. Khan, A.A., Arora, A.S.: Thermography as an economical alternative modality to mammography for early detection of breast cancer. J. Healthc. Eng. 2021, 1–8 (2021)

    Google Scholar 

  31. Ursin, G., Hovanessian-Larsen, L., Parisky, Y.R., Pike, M.C., Wu, A.H.: Greatly increased occurrence of breast cancers in areas of mammographically dense tissue. Breast Cancer Res. 7, R605 (2005)

    Article  Google Scholar 

  32. Kiymet, S., Aslankaya, M.Y., Taskiran, M., Bolat, B.: Breast cancer detection from thermography based on deep neural networks. In: 2019 Innovations in Intelligent Systems and Applications Conference (ASYU), pp. 1–5. IEEE (2019) https://doi.org/10.1109/ASYU48272.2019.8946367

  33. de Vasconcelos, J.H., dos Santos, W.P., de Lima, R.C.F.: Analysis of methods of classification of breast thermographic images to determine their viability in the early breast cancer detection. IEEE Lat. Am. Trans. 16, 1631–1637 (2018)

    Article  Google Scholar 

  34. Souza Marques, R., Conci, A., Perez, M.G., Andaluz, V.H., Mejia, T.M.: An approach for automatic segmentation of thermal imaging in computer aided diagnosis. IEEE Lat. Am. Trans. 14, 1856–1865 (2016)

    Article  Google Scholar 

  35. Wakankar, A., Suresh, G.R., Ghugare, A.: Automatic diagnosis of breast abnormality using digital IR camera. In: 2014 International Conference on Electronic Systems, Signal Processing and Computing Technologies, pp. 145–150. IEEE (2014). https://doi.org/10.1109/ICESC.2014.30

  36. 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 International Symposium on Advanced Computing and Communication (ISACC), pp. 258–265. IEEE (2015) https://doi.org/10.1109/ISACC.2015.7377351

  37. Yassin, N.I.R., Omran, S., El Houby, E.M.F., Allam, H.: Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: a systematic review. Comput. Methods Programs Biomed. 156, 25–45 (2018)

    Article  Google Scholar 

  38. Comelli, A., et al.: Automatic multi-seed detection for MR breast image segmentation. In: Battiato, S., Gallo, G., Schettini, R., Stanco, F. (eds.) ICIAP 2017. LNCS, vol. 10484, pp. 706–717. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68560-1_63

    Chapter  Google Scholar 

  39. Oza, P., Sharma, P., Patel, S., Bruno, A.: A Bottom-up review of image analysis methods for suspicious region detection in mammograms. J. Imaging 7, 190 (2021)

    Article  Google Scholar 

  40. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440. IEEE (2015). https://doi.org/10.1109/CVPR.2015.7298965

  41. Xu, S., et al.: Mammographic mass segmentation using multichannel and multiscale fully convolutional networks. Int. J. Imaging Syst. Technol. 30, 1095–1107 (2020)

    Article  Google Scholar 

  42. Utomo, A., Juniawan, E.F., Lioe, V., Santika, D.D.: Local features based deep learning for mammographic image classification: in comparison to CNN models. Procedia Comput. Sci. 179, 169–176 (2021)

    Article  Google Scholar 

  43. Lee, J., Nishikawa, R.M.: Automated mammographic breast density estimation using a fully convolutional network. Med. Phys. 45, 1178–1190 (2018)

    Article  Google Scholar 

  44. Al-Antari, M.A., Al-Masni, M.A., Choi, M.-T., Han, S.-M., Kim, T.-S.: A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification. Int. J. Med. Inf. 117, 44–54 (2018)

    Article  Google Scholar 

  45. Kassani, S.H., Kassani, P.H., Wesolowski, M.J., Schneider, K.A., Deters, R.: Breast cancer diagnosis with transfer learning and global pooling. ArXiv190911839 Cs Eess (2019)

    Google Scholar 

  46. Zuluaga-Gomez, J., Al Masry, Z., Benaggoune, K., Meraghni, S., Zerhouni, N.: A CNN-based methodology for breast cancer diagnosis using thermal images. Comput. Methods Biomech. Biomed. Eng. Imaging Vis. 9, 131–145 (2021)

    Google Scholar 

  47. Silva, L.F., et al.: A new database for breast research with infrared image. J. Med. Imaging Health Inform. 4, 92–100 (2014)

    Article  Google Scholar 

  48. de Marques, R.S.: Automatic segmentation of thermal mammogram images, Dissertation (2012)

    Google Scholar 

  49. Visual Lab DMR (2019). http://visual.ic.uff.br/dmi/

  50. Martin del Campo Mena, E. Digital Infrared Analysis (2014). https://www.blogger.com/profile/06826574535066522830

  51. 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)

    Article  Google Scholar 

  52. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale Image Recognition. ArXiv14091556 Cs (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. AITalos, 7926-2785 Commercial Drive, Vancouver, BC, V5N 4C5, Canada

    Punitee Garyali, Iman Ranjbar & Seyedreza Movahedi

Authors
  1. Punitee Garyali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Iman Ranjbar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Seyedreza Movahedi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Seyedreza Movahedi .

Editor information

Editors and Affiliations

  1. Niramai Health Analytix Pvt. Ltd., Bengaluru, India

    Siva Teja Kakileti

  2. IBM Research - Zurich, Rüschlikon, Switzerland

    Maria Gabrani

  3. Niramai Health Analytix Pvt. Ltd., Bengaluru, India

    Geetha Manjunath

  4. University of Haifa, Haifa, Israel

    Michal Rosen-Zvi

  5. Picture Health, Cleveland, OH, USA

    Nathaniel Braman

  6. Piedmont Physical Medicine and Rehabilitation, Greenville, SC, USA

    Robert G. Schwartz

  7. University of Leeds, Leeds, UK

    Alejandro F. Frangi

  8. National Cheng Kung University, Tainan, Taiwan

    Pau-Choo Chung

  9. Cleveland Clinic, Cleveland, OH, USA

    Christopher Weight

  10. Tempus Labs, Menlo Park, CA, USA

    Vekataraman Jagadish

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Garyali, P., Ranjbar, I., Movahedi, S. (2022). A Novel Thermography-Based Artificial Intelligence-Powered Solution for Screening Breast Cancer. In: Kakileti, S.T., et al. Artificial Intelligence over Infrared Images for Medical Applications and Medical Image Assisted Biomarker Discovery. MIABID AIIIMA 2022 2022. Lecture Notes in Computer Science, vol 13602. Springer, Cham. https://doi.org/10.1007/978-3-031-19660-7_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-19660-7_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19659-1

  • Online ISBN: 978-3-031-19660-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation