Skip to main content
SpringerLink
Log in
SpringerLink
Log in

Fuzzy Ordination of Breast Tissue with Electrical Impedance Spectroscopy Measurements

  • Conference paper
  • First Online:
10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions - ICSCCW-2019 (ICSCCW 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((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.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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. Morris, E., Liberman, L.: Breast MRI. Springer, New York (2005)

    Google Scholar 

  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.2015.10.005

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  5. Zarafshani, A., Bach, T., Chatwin, C.R., Tang, S., Xiang, L., Zheng, B.: Conditioning electrical impedance mammography system. Measurement 116, 38–48 (2018)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. UCI (California Irvine University). http://archive.ics.uci.edu/ml/datasets/breast+tissue. Accessed 10 Jan 2019

  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. 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. Jantzen, J.: Tutorial on Fuzzy Logic. Technical University of Denmark (2008)

    Google Scholar 

  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 

Download references

Author information

Authors and Affiliations

  1. Faculty of Medicine, Department of Biostatistics, Near East University, Near East Boulevard, P.O. Box: 99138, Nicosia, TRNC, Mersin 10, Turkey

    Meliz Yuvalı

  2. Department of Electrical and Electronic Engineering, Near East University, Near East Boulevard, P.O. Box: 99138, Nicosia, TRNC, Mersin 10, Turkey

    Cemal Kavalcıoğlu

  3. Department of Biomedical Engineering, Near East University, Near East Boulevard, P.O. Box: 99138, Nicosia, TRNC, Mersin 10, Turkey

    Şerife Kaba & Ali Işın

Authors
  1. Meliz Yuvalı
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cemal Kavalcıoğlu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Şerife Kaba
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ali Işın
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Meliz Yuvalı .

Editor information

Editors and Affiliations

  1. Joint MBA Program, Azerbaijan State Oil and Industry University, Baku, Azerbaijan

    Rafik A. Aliev

  2. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    Janusz Kacprzyk

  3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  4. Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX, USA

    Mo Jamshidi

  5. Azerbaijan State Oil and Industry University, Baku, Azerbaijan

    Mustafa B. Babanli

  6. Department of Mechatronics, Near East University, Nicosia, Turkey

    Fahreddin M. Sadikoglu

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yuvalı, M., Kavalcıoğlu, C., Kaba, Ş., Işın, A. (2020). Fuzzy Ordination of Breast Tissue with Electrical Impedance Spectroscopy Measurements. In: Aliev, R., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Babanli, M., Sadikoglu, F. (eds) 10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions - ICSCCW-2019. ICSCCW 2019. Advances in Intelligent Systems and Computing, vol 1095. Springer, Cham. https://doi.org/10.1007/978-3-030-35249-3_19

Download citation

Publish with us

Policies and ethics

Search

Navigation