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Precise detection of early breast tumor using a novel EEMD-based feature extraction approach by UWB microwave

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Abstract

The accurate detection of early breast cancer is of great significance to each patient. In recent years, breast cancer non-invasive detection technology based on Ultra-Wideband (UWB) microwave has been proposed and developed extensively, which is complementary to the existing methods. In this paper, a novel approach is proposed for tumor existence detection based on feature extraction algorithm. Firstly, the breast features are obtained by Ensemble Empirical Mode Decomposition (EEMD) and valid correlation Intrinsic Mode Function (IMF) selection. Secondly, raw feature datasets are constructed and then simplified by Principal Component Analysis (PCA) or Recursive Feature Elimination (RFE). Finally, the detection is realized by Support Vector Machines (SVM). The influence of different kernel functions and feature selection methods on detection results is compared. In this study, 11,232 sets of backscatter signals from simulation results of four different categories’ breast models are utilized. And feature dataset is constructed by 24 specific features from each signal’s four valid components. The results demonstrate that the proposed method can extract representative features and detect the early breast cancer effectively with the accuracy of 84.8%.

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References

  1. Rebecca LS, Kimberly DM, Ahmedin J (2019) Breast cancer statistics, 2019. A cancer journal for clinicians 69(6):438–451

    Article  Google Scholar 

  2. Hu K, Ding P, Wu Y, Tian W, Pan T, Zhang S (2019) Global patterns and trends in the breast cancer incidence and mortality according to sociodemographic indices: an observational study based on the global burden of diseases. BMJ Open 9(10):028461

    Google Scholar 

  3. Hwang ES, Lichtensztajn DY, Gomez SL, Fowble B, Clarke CA (2013) Survival after lumpectomy and mastectomy for early stage invasive breast cancer. Cancer 119(7):1402–1411

    Article  Google Scholar 

  4. Moss SM, Wale C, Smith RA, Evans A, Cuckle H, Duffy SW (2015) Effect of mammographic screening from age 40 years on breast cancer mortality in the UK Age trial at 17 years’ follow-up: a randomised controlled trial. Lancet Oncol 16(9):1123–1132

    Article  Google Scholar 

  5. Brovoll S, Berger T, Paichard Y, Aardal Ø, Lande TS, Hamran SE (2014) Time-lapse imaging of human heart motion with switched array UWB radar. IEEE Transactions on Biomedical Circuits and Systems 8(5):704–715

    Article  Google Scholar 

  6. Sugitani T, Kubota SI, Kuroki SI, Sogo K, Arihiro K (2014) Complex permittivities of breast tumor tissues obtained from cancer surgeries. Appl Phys Lett 104(25):253702

    Article  Google Scholar 

  7. Islam MM, Islam MT, Samsuzzaman MI, Faruq MR (2015) Five band-notched ultrawide band (UWB) antenna loaded with C-shaped slots. Microw Opt Technol Lett 57(6):1470–1475

    Article  Google Scholar 

  8. Hagness SC, Taflove A, Bridges JE (1998) 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(12):1470–1479

    Article  CAS  Google Scholar 

  9. Byrne D, Craddock IJ (2015) Time-domain wideband adaptive beamforming for radar breast imaging. IEEE Trans Antennas Propag 63(4):1725–1735

    Article  Google Scholar 

  10. Kosmas P, Rappaport CM (2006) A matched-filter FDTD-based time reversal approach for microwave breast cancer detection. IEEE Trans Antennas Propag 54(4):1257–1264

    Article  Google Scholar 

  11. Meaney PM, Fanning MW, Raynolds T, Fox CJ, Fang Q, Kogel CA, Poplack SP, Paulsen KD (2007) Initial clinical experience with microwave breast imaging in women with normal mammography. Acad Radiol 14(2):207–218

    Article  Google Scholar 

  12. Byrne D, Ohalloran M, Jones E, Glavin M (2011) Support vector machine-based ultrawideband breast cancer detection system. Journal of Electromagnetic Waves and Applications 25(13):1807–1816

    Article  Google Scholar 

  13. Aydin A, Avşar Aydin E (2017) Evaluation of limestone layer’s effect for uwb microwave imaging of breast models using neural network. Tehnički glasnik 11(1-2):50–54

    Google Scholar 

  14. Song H, Li Y, Men A (2018) Microwave breast cancer detection using time-frequency representations. Med Biol Eng Comput 56(4):571–582

    Article  Google Scholar 

  15. Conceicao RC, Ohalloran M, Jone E, Glavin M (2010) Investigation of classifiers for early-stage breast cancer based on radar target signatures. Prog Electromagn Res 23:311–327

    Article  Google Scholar 

  16. Conceicao RC, Ohalloran M, Jone E, Glavin M (2011) Evaluation of features and classifiers for classification of early-stage breast cancer. Journal of Electromagnetic Waves and Applications 25(1):1–14

    Article  Google Scholar 

  17. Wu Z, Huang NE (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 1(1):1–41

    Article  Google Scholar 

  18. Žvokelj M, Zupan S, Prebil I (2016) EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis. J Sound Vib 370:394–423

    Article  Google Scholar 

  19. Chen X, Chen Q, Zhang Y, Wang Z (2018) A novel EEMD-CCA approach to removing muscle artifacts for pervasive EEG. IEEE Sensors J 19(19):8420–8431

    Article  Google Scholar 

  20. Zhang W, Zhang X, Sun Y (2012) Based on EEMD-HHT marginal spectrum of speech emotion recognition. 2012 International Conference on Computing, Measurement Control and Sensor Network 91-94

  21. Turnip A, Siahaan M (2014) Adaptive principal component analysis based recursive least squares for artifact removal of EEG signals. Adv Sci Lett 20(10-11):2034–2037

    Article  Google Scholar 

  22. Wang B, Liu X, Yu B, Jia R, Gan X (2018) Pedestrian dead reckoning based on motion mode recognition using a smartphone. Sensors 18(6):1181

    Google Scholar 

  23. Yan K, Zhang D (2015) Feature selection and analysis on correlated gas sensor data with recursive feature elimination. Sensors Actuators B Chem 212:353–363

    Article  CAS  Google Scholar 

  24. Ryu KS, Kishk AA (2011) UWB dielectric resonator antenna having consistent omnidirectional pattern and low cross-polarization characteristics. IEEE Trans Antennas Propag 59(4):1403–1408

    Article  Google Scholar 

  25. Bahramiabarghouei H, Porter E, Santorelli A, Gosseli B, Popovi ́c M, Rusch RA (2015) Flexible 16 antenna array for microwave breast cancer detection. IEEE Trans Biomed Eng 62(10):2516–2525

    Article  Google Scholar 

  26. Wang Z, Xiao X, Song H, Wang L, Li Q (2014) Development of anatomically realistic numerical breast phantoms based on T1 and T2 weighted MRIs for microwave breast cancer detection. IEEE Antennas and Wireless Propagation Letters 13:1757–1760

    Article  Google Scholar 

  27. Xiao X, Song H, Wang ZJ, Wang L (2014) A progressive processing method for breast cancer detection via UWB based on an MRI-derived model. Chin Phys B 7(52):074101

    Article  Google Scholar 

  28. Li Q, Xiao X, Wang L, Song H, Kono H, Liu P (2015) Direct extraction of tumor response based on ensemble empirical mode decomposition for image reconstruction of early breast cancer detection by UWB. IEEE transactions on biomedical circuits and systems 9(5):710–724

    Article  Google Scholar 

  29. Sugitani T, Kubota S, Toya A, Xiao X, Kikkawa T (2013) A compact 4x4 planar UWB antenna array for 3-D breast cancer detection. IEEE Antennas and Wireless Propagation Letters 12:733–736

    Article  Google Scholar 

  30. Huang NE, Shen Z, Long SR, Wu MC (1971) Shih SS (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis. Proceedings A 454:903–995

    Google Scholar 

  31. Kedadouche M, Thomas M, Tahan A (2016) A comparative study between empirical wavelet transforms and empirical mode decomposition methods: application to bearing defect diagnosis. Mech Syst Signal Process 81:88–107

    Article  Google Scholar 

  32. Zhou H, Deng Z, Xia Y, Fu M (2016) A new sampling method in particle filter based on Pearson correlation coefficient. Neurocomputing 216:208–215

    Article  Google Scholar 

  33. Huang S, Chang J, Huang Q, Chen Y (2014) Monthly streamflow prediction using modified EMD-based support vector machine. J Hydrol 511:764–775

    Article  Google Scholar 

  34. Patel R, Sengottuvel S, Janawadkar MP, Gireesan K, Radhakrishnan TS, Mariyappa N (2016) Ocular artifact suppression from EEG using ensemble empirical mode decomposition with principal component analysis. Comput Electr Eng 54:78–86

    Article  Google Scholar 

  35. Guyon I, Weston J, Barnhill S (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46(1-3):389–422

    Article  Google Scholar 

Download references

Acknowledgements

The authors are grateful to the National Natural Science Foundation of China (Grant No. 61271323) and Innovation Project of Tianjin University (Grant No. 2020XYF-0037).

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Correspondence to Xia Xiao or Hang Song.

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Liu, G., Xiao, X., Song, H. et al. Precise detection of early breast tumor using a novel EEMD-based feature extraction approach by UWB microwave. Med Biol Eng Comput 59, 721–731 (2021). https://doi.org/10.1007/s11517-021-02339-5

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  • DOI: https://doi.org/10.1007/s11517-021-02339-5

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