Hostname: page-component-78c5997874-dh8gc Total loading time: 0 Render date: 2024-11-16T01:21:46.091Z Has data issue: false hasContentIssue false
  • English
  • Français

ANN-based radar approach to detect breast cancers in fibro-glandular tissues: numerical analysis

Published online by Cambridge University Press:  22 August 2017

Salvatore Caorsi  and
Claudio Lenzi Open the ORCID record for Claudio Lenzi [Opens in a new window]
Salvatore Caorsi
Affiliation:
Department of Electrical, Computer and Biomedical Engineering, University of Pavia, via Ferrata 5, 27100 Pavia, Italy
Claudio Lenzi*
Affiliation:
Department of Electrical, Computer and Biomedical Engineering, University of Pavia, via Ferrata 5, 27100 Pavia, Italy
*
Corresponding author: C. Lenzi Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper presents a new artificial neural network (ANN)-based radar data processing approach for the detection of breast cancers located inside fibro-glandular tissues. The aim is not the breast imaging but detecting tumors through ANN processing of data extracted from the radar signals measured around the breast. The proposed approach has been assessed using several realistic two-dimensional breast geometries derived from the models provided by the numerical breast phantom repository of the University of Wisconsin Cross-Disciplinary Electromagnetic Laboratory (UWCEM). The pulsed radar system was assumed to operate in the mono-static configuration. The obtained results showed the abilities of the proposed approach to detect, for any single radar trace, tumors located inside the fibro-glandular tissues with a sensitivity of 93%, a specificity of 90%, and an overall accuracy of 92%.

Keywords

Breast cancer detectionInverse scatteringArtificial neural network
Type
Research Papers
Information
International Journal of Microwave and Wireless Technologies , Volume 9 , Special Issue 8: Biomedical Applications of RF/Microwave and Optics Technologies , October 2017 , pp. 1597 - 1602
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

[1]American Cancer Society, Cancer Facts & Figures 2015, Atlanta, GA, USA, American Cancer Society, 2015.Google Scholar
[2] Hassan, A.M.; El-Shenawee, M.: Review of electromagnetic techniques for breast cancer detection. IEEE Rev. Biomed. Eng., 4 (2011), 103–118.Google Scholar
[3] Hagness, S.C.; Taflove, A.; Bridges, J.E.: Two-dimensional FDTD analysis of a pulsed microwave confocal system for breast cancer detection: fixed-focus and antenna-array sensors. IEEE Trans. Biomed. Eng., 45 (1998), 1470–1479.CrossRefGoogle ScholarPubMed
[4] Shea, J.D.; Van Veen, B.D.; Hagness, S.C.: A TSVD analysis of microwave inverse scattering for breast imaging. IEEE Trans. Biomed. Eng., 59 (2011), 936–945.Google Scholar
[5] Fang, Q.; Meaney, P.M.; Paulsen, K.D.: Viable three-dimensional medical microwave tomography: theory and numerical experiments. IEEE Trans. Antennas Propag., 58 (2010), 449–458.CrossRefGoogle ScholarPubMed
[6] Fear, E.C.; Li, X.; Hagness, S.C.; Stuchly, M.A.: Confocal microwave imaging for breast cancer detection: localization of tumors in three dimensions. IEEE Trans. Biomed. Eng., 49 (2002), 812–822.Google Scholar
[7] Yin, T.; Ali, F.H.; Reyes-Aldasoro, C.C.: A robust and artifact resistant algorithm of ultrawideband imaging system for breast cancer detection. IEEE Trans. Biomed. Eng., 62 (2015), 1514–1525.CrossRefGoogle Scholar
[8] Caorsi, S.; Lenzi, C.: Can an ANN based radar data processing approach be an aid in breast cancer detection?, in IEEE Int. Conf. on Electromagnetics in Advanced Applications (ICEAA), Turin, Italy, 2015.Google Scholar
[9] Giannopoulos, A.: Modelling ground penetrating radar by GprMax. Constr. Build. Mater., 19 (2005), 755–762.Google Scholar
[10] Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice-Hall, New York, NY, USA, 1994.Google Scholar
[11] Li, X.; Hagness, S.C.: A confocal microwave imaging algorithm for breast cancer detection. IEEE Microw. Wireless Compon. Lett., 11 (2001), 130–132.Google Scholar
[12] Zhi, W.; Chin, F.: Entropy-based time window for artifact removal in UWB imaging of breast cancer detection. IEEE Signal Process. Lett., 13 (2006), 585–588.Google Scholar
[13] Uduwawala, D.: Gaussian vs differentiated gaussian as the input pulse for ground penetrating radar applications, in Int. Conf. on Industrial and Information Systems (ICIIS), Penadeniya, Sri Lanka, 2007.Google Scholar
[14] Zastrow, E.; Davis, S.K.; Lazebnik, M.; Kelcz, F.; Van Veem, B.D.; Hagness, S.C.: Database of 3D Grid-Based Numerical Breast Phantoms for use in Computational Electromagnetics Simulations, Department of Electrical and Computer Engineering University of Wisconsin-Madison. http://uwcem.ece.wisc.edu/MRIdatabase/InstructionManual.pdf.Google Scholar
[15] Lazebnik, M.; Popovic, D.; McCartney, L.; Watkins, C.B.; Lindstrom, M.J.; Harter, J. et al. : A large-scale study of the ultrawideband microwave dielectric properties of normal, benign, and malignant breast tissues obtained from cancer surgeries. Phys. Med. Biol., 52 (2007), 6093–6115.CrossRefGoogle ScholarPubMed
[16] Lazebnik, M.; Okoniewski, M.; Booske, J.H.; Hagness, S.C.: Highly accurate Debye models for normal and malignant breast tissue dielectric properties at microwave frequencies. IEEE Microw. Wireless Compon. Lett., 17 (2007), 822–824.CrossRefGoogle Scholar
[17] Maskooki, A.; Gunawan, E.; Soh, S.B.; Low, K.S.: Frequency domain skin artifact removal method for ultra-wideband breast cancer detection. Prog. Electromagn. Res., 98 (2009), 299–314.Google Scholar
[18] Maklad, B.; Fear, E.C.: Reduction of skin reflections in radar-based microwave breast imaging, in IEEE Int. Conf. of the Engineering in Medicine and Biology Society (EMBS), Vancouver, BC, 2008.Google Scholar
[19] Klemm, M.; Craddock, I.J.; Leendertz, J.A.; Preece, A.; Benjamin, R.: Improved delay-and-sum beamforming algorithm for breast cancer detection. Int. J. Antennas Propag., 2008 (2008), 1–9.CrossRefGoogle Scholar
[20] Bond, E.J.; Li, X.; Hagness, S.C.; Van Veen, B.D.: Microwave imaging via space-time beamforming for early detection of breast cancer. IEEE Trans. Antennas Propag., 51 (2003), 1690–1705.Google Scholar
[21] Caorsi, S.; Lenzi, C.: Skin artifact removal technique for breast cancer radar detection. Radio Sci., 51 (2016), 767–778.CrossRefGoogle Scholar
[22] Richards, M.A.; Scheer, J.A.; Holm, W.A.: Principles of modern radar. SciTech Pub, Raleigh, NC, 2010.Google Scholar