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Fuzzy Ordination of Breast Tissue with Electrical Impedance Spectroscopy Measurements

  • Meliz YuvalıEmail author
  • Cemal Kavalcıoğlu
  • Şerife Kaba
  • Ali Işın
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1095)

Abstract

Electrical impedance spectroscopy (EIS) is a useful technique which requires minimum invasion into body employed for the characterization of living tissues with the facility and low cost. EIS techniques assist diagnosis, in a different way, by providing information regarding the electrical conductivity and permittivity properties of the patient’s cells and tissues. Measuring the bio-impedance of the tissues, allows scientists to take into consideration the capacitive characteristics of the tissues along with their resistive characteristics. So by effectively measuring electrical impedances through body tissues, cancerous tissues can be differentiated and diagnosed. In this study, breast tissues obtained from 106 patients have been classified via fuzzy logic according to the data accumulation in the EIS device. The device gives 9 different impedance features for each patient which are then reduced to 6 classes. These classes are; glandular tissue, connective tissue, adipose, mastopathy, fibro-adenoma and carcinoma. The aim of this study is to design a fuzzy system to classify breast tissues with EIS test results.

Keywords

Breast tissue Electrical Impedance Spectroscopy Fuzzy logic 

References

  1. 1.
    Morris, E., Liberman, L.: Breast MRI. Springer, New York (2005)Google Scholar
  2. 2.
    Sibbering, M., Courtney, C.: Management of breast cancer: basic principles. Surgery (Oxford) 34(1), 25–31 (2016).  https://doi.org/10.1016/j.mpsur.2015.10.005CrossRefGoogle Scholar
  3. 3.
    Da Silva, J.E., De Sá, J.M., Jossinet, J.: Classification of breast tissue by electrical impedance spectroscopy. Med. Biol. Eng. Comput. 38(1), 26–30 (2000)CrossRefGoogle Scholar
  4. 4.
    Moqadam, S.M., Grewal, P.K., Haeri, Z., Ingledew, P.A., Kohli, K., Golnaraghi, F.: Cancer detection based on electrical impedance spectroscopy: a clinical study. J. Electr. Bioimp. 9, 17–23 (2018)CrossRefGoogle Scholar
  5. 5.
    Zarafshani, A., Bach, T., Chatwin, C.R., Tang, S., Xiang, L., Zheng, B.: Conditioning electrical impedance mammography system. Measurement 116, 38–48 (2018)CrossRefGoogle Scholar
  6. 6.
    Morimoto, T., Kimura, S., Konishi, Y., Komaki, K., Uyama, T., Monden, Y., Kinouchi, D.Y., Iritani, D.T: A study of the electrical bioimpedance of tumors. J. Invest. Surg. 6, 25–32 (1993)CrossRefGoogle Scholar
  7. 7.
    Liu, C., Chang, T., Li, C.: Breast tissue classification based on electrical impedance spectroscopy. In: 2015 International Conference on Industrial Technology and Management Science. Atlantis Press (2015)Google Scholar
  8. 8.
    Gao, W., Fan, M., Zhao, W., Zheng, B., Li, L.: Computerized detection of breast cancer using resonance-frequency-based electrical impedance spectroscopy. In: Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications, vol. 10138, p. 1013816. International Society for Optics and Photonics (2017)Google Scholar
  9. 9.
    UCI (California Irvine University). http://archive.ics.uci.edu/ml/datasets/breast+tissue. Accessed 10 Jan 2019
  10. 10.
    Adedeji, B., Badiru, J.Y.C.: Fuzzy Engineering expert systems with Neural Network Applications. Department of Industrial Engineering University of Tennessee Knoxville, TN. School of Electrical and Computer Engineering University of Oklahoma Norman, OK (2002)Google Scholar
  11. 11.
    Sandya, H.B., Hemanth Kumar, P., Himanshi Bhudiraja, S.K.R.: Fuzzy rule based feature extraction and classification of time series signal. Int. J. Soft Comput. Eng. (IJSCE) 3, 2231–2307 (2013)Google Scholar
  12. 12.
    Jantzen, J.: Tutorial on Fuzzy Logic. Technical University of Denmark (2008)Google Scholar
  13. 13.
    Güler, I., Ubeyli, D.E.: Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients. A Department of Electronics and Computer Education, Faculty of Technical Education, Gazi University, Ankara, Turkey, Department of Electrical and Electronics Engineering, Faculty of Engineering (2005)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Medicine, Department of BiostatisticsNear East UniversityNicosiaTurkey
  2. 2.Department of Electrical and Electronic EngineeringNear East UniversityNicosiaTurkey
  3. 3.Department of Biomedical EngineeringNear East UniversityNicosiaTurkey

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