Breast Cancer Detection Using Modern Visual IT Techniques

  • Sebastien Mambou
  • Petra Maresova
  • Ondrej KrejcarEmail author
  • Ali Selamat
  • Kamil Kuca
Part of the Studies in Computational Intelligence book series (SCI, volume 769)


Nowadays, cancer is a major cause of women death, especially breast cancer which is most seen on ladies older than 40 years. As we know, several techniques have been developed to fight breast cancer, like a mammography, which is the preferred screening examination for breast cancer. However, despite mammography test showing negative result, there are still patients with breast cancer diagnostic, found by other tests like ultrasound test. It can be explained by potential side effect of using mammography, which can push patients and doctors to look for other diagnostic technique. In this literature review, we will explore the digital infrared imaging which is based on the principle that metabolic activity and vascular circulation, in both pre-cancerous tissue and the area surrounding a developing breast cancer, is almost always higher than in normal breast tissue. In the same way, an automated infrared image processing of patient cannot be done without a model like the hemispheric model, which is very well known. As novelty, we will give a comparative study of breast cancer detection using modern visual IT techniques view by the perspective of computer scientist.


Breast Cancer Detection Visual techniques Neural network SVM 



This work was supported by internal students project at FIM, University of Hradec Kralove, Czech Republic (under ID: UHK-FIM-SP-2018).


  1. 1.
    Breast Cancer Facts, National Breast Cancer Foundation (2016)Google Scholar
  2. 2.
    Dongola, N.: Mammography in Breast Cancer. Medscape Logo (2016)Google Scholar
  3. 3.
    Köşüş, N., Köşüş, A., Duran, M., Simavlı, S., Turhan, N.: Comparison of standard mammography with digital mammography and digital infrared thermal imaging for breast cancer screening. J. Turk. Ger. Gynecol. Assoc. (2010)Google Scholar
  4. 4.
    Li, S., Johnson, J., Peck, A., Xie, Q.: Near infrared fluorescent imaging of brain tumor with IR780 dye incorporated phospholipid nanoparticles. J. Trans. Med. (2017)Google Scholar
  5. 5.
    Amria, A., Pulko, S.H., Wilk, A.J.: Potentialities of steady-state and transient thermography in breast tumour depth detection: a numerical study. Comput. Methods Programs Biomed. (2016)Google Scholar
  6. 6.
    Boogerd, L.S.F., Handgraaf, H.J.M., Lam, H.-D., Huurman, V.A.L., Farina-Sarasqueta, A., Frangioni, J.V., van de Velde, C.J.H., Braat, A.E., Vahrmeijer, A.L.: Laparoscopic detection and resection of occult liver tumors of multiple cancer types using real-time near-infrared fluorescence guidance. Surg. Endosc. (2017)Google Scholar
  7. 7.
    Kandlikar, S.G., Perez-Raya, I., Raghupathi, P.A., Gonzalez-Hernandez, J.L., Dabydeen, D., Medeiros, L., Phatak, P.: Infrared imaging technology for breast cancer detection—Current status, protocols and new directions. Int. J. Heat Mass Trans. (2017)Google Scholar
  8. 8.
    Tsutomu Namikawa, T.S.: Recent advances in near-infrared fluorescence-guided imaging surgery using indocyanine green. Surg. Today (2015)Google Scholar
  9. 9.
    Kontos, M., Wilson, R., Fentiman, I.: Digital infrared thermal imaging (DITI) of breast lesions: sensitivity and specificity of detection of primary breast cancers. Clin. Radiol. (2011)Google Scholar
  10. 10.
    Łyszczarz, B., Nojszewska, E.: Productivity losses and public finance burden attributable to breast cancer in Poland, 2010–2014. BMC Cancer 17(1), 676 (2017)Google Scholar
  11. 11.
    National Oncology Program. Czech Oncological Society (2011)Google Scholar
  12. 12.
    Unar-Munguía, M., Meza, R., Colchero, M.A., et al.: Economic and disease burden of breast cancer associated with suboptimal breastfeeding practices in Mexico. Cancer Causes Control (2017)Google Scholar
  13. 13.
    Boquete, L., Ortega, S., Miguel-Jiménez, J.M., Rodríguez-Ascariz, J.M.: Automated detection of breast cancer in thermal infrared images, based on independent component analysis. J. Med. Syst. (2012)Google Scholar
  14. 14.
    Kubicek, J., Bryjova, I., Faltynova, K., Penhaker, M., Augustynek, M., Maresova, P.: Evaluation of gama analysis results significance within verification of radiation IMRT plans in radiotherapy. Lecture Notes in Computer Science, vol. 10449, pp. 541–548 (2017).
  15. 15.
    Augustynek, M., Korpas, D., Penhaker, M., Cvek, J., Binarova, A.: Monitoring of CRT-D devices during radiation therapy in vitro. BioMedical Engineering Online, 15 (1), article no. 29 (2016).
  16. 16.
    Smidova, I.: Alcohol and breast cancer—economic costs. Hygiena 51(1), 17–21 (2012)Google Scholar
  17. 17.
    Gustavsen, G., Schroeder, B., Kennedy, P., et al.: Health economic analysis of breast cancer index in patients with ER+, LN− breast cancer. Am. J. Manag. Care 20(8), 1 (2014)Google Scholar
  18. 18.
    Kim, Y.A., Oh, I.H., Yoon, S.J., et al.: The economic burden of breast cancer in Korea from 2007–2010. Cancer Res. Treat. 47(4), 583–590 (2015)CrossRefGoogle Scholar
  19. 19.
    Bryjova, I., Kubicek, J., Molnarova, K., Peter, L., Penhaker, M., Kuca, K.: Multiregional segmentation modeling in medical ultrasonography: extraction, modeling and quantification of skin layers and hypertrophic scars. Lecture Notes in Computer Science, vol. 10449, LNAI, pp. 182–192 (2017).
  20. 20.
    IMS Health, MIDAS, Dec 2015; Market Prognosis, Mar 2016. IMS Institute for Healthcare Informatics, May 2016Google Scholar
  21. 21.
    Cardoso, F., Harbeck, N., Bergh, J., Cortés, J.: Research needs in breast cancer. Ann. Oncol. (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Faculty of Informatics and ManagementCenter for Basic and Applied Research, University of Hradec KraloveHradec KraloveCzech Republic
  2. 2.Faculty of Informatics and Management, Department of EconomyUniversity of Hradec KraloveHradec KraloveCzech Republic
  3. 3.Faculty of ComputingUniversiti Teknologi MalaysiaJohor BahruMalaysia

Personalised recommendations