iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://unpaywall.org/10.1007/978-3-030-46970-2_13
Application of Data Mining and Machine Learning in Microwave Radiometry (MWR) | SpringerLink
Skip to main content
Springer Nature Link
Log in

Application of Data Mining and Machine Learning in Microwave Radiometry (MWR)

  • Conference paper
  • First Online:
Biomedical Engineering Systems and Technologies (BIOSTEC 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1211))

  • 606 Accesses

Abstract

Microwave radiometry has seen its way in successful usage in medical applications. The focus here is its applicability in cancer detection and monitoring, specifically for breast cancer, as an additional and alternative tool. This is done by capturing the temperature of the skin and the internal tissue. However, the amount of data required by clinical specialist to process in a short time to reach to a confident decision is becoming insurmountable. This can be tackled by developing a diagnostic system that will help pinpoint irregularities associated with pathologies. The key factors of a successful diagnostic system is the accuracy of the predictions and its informativeness and interpretability. The core component of such a system is a machine learning algorithm. Models that were explored were random forest, k-nearest neighbors, support vector machines, variants of cascade correlation neural networks, deep neural network and convolution neural network. From all these models evaluated, the best performing on the test set was the deep neural network. Also, we proposed a method for forming the space of thermometric features, which at the same time ensures a sufficiently high efficiency of the classification algorithms. More importantly, the model is inherently able to provide an explanation of the diagnostic solution.

AL and VL are grateful to Russian Foundation for Basic Research (grant No RFBR 19-01-00358) for the financial support of the development of mathematical models for early diagnosis of breast cancer.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Similar content being viewed by others

References

  1. Abadi, M., et al.: TensorFlow: a system for large-scale machine learning. In: Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation, OSDI 2016, pp. 265–283. USENIX Association, Berkeley (2016)

    Google Scholar 

  2. Anisimova, E.V., Zamechnik, T.V., Larin, S.I., Losev, A.G.: Teoreticheskie issledovaniya otdelnih fizicheskih i fiziologicheskih faktorov vliyayuschih na kachestvo obsledovaniya pacientov s varikoznoi boleznyu ven nijnih konechnostei metodom kombinirovannoi termografii [the theoretical research of separate physical and physiological factors influencing the quality of checking up patients with venous varicosity of lower extremities by the method of combined thermography]. Vestnik novih medicinskih tehnologii [J. New Med. Technol.] 18(4), 280–282 (2011)

    Google Scholar 

  3. Arajo, T., et al.: Classification of breast cancer histology images using convolutional neural networks. PLoS ONE 12(6), 1–14 (2017)

    Google Scholar 

  4. Barandela, R., Sanchez, J., Garca, V., Rangel, E.: Strategies for learning in class imbalance problems. Pattern Recogn. 36, 849–851 (2003)

    Article  Google Scholar 

  5. Bergstra, J., Bardenet, R., Bengio, Y., Kégl, B.: Algorithms for hyper-parameter optimization. In: Proceedings of the 24th International Conference on Neural Information Processing Systems, NIPS 2011, pp. 2546–2554. Curran Associates Inc., USA (2011)

    Google Scholar 

  6. Bergstra, J., Yamins, D., Cox, D.D.: Hyperopt: a python library for optimizing the hyperparameters of machine learning algorithms (2015)

    Google Scholar 

  7. Bishop, C.M.: Pattern Recognition and Machine Learning. Information Science and Statistics. Springer, Heidelberg (2006)

    MATH  Google Scholar 

  8. Bochkarev, O.A., Zenovich, A.V., Losev, A.G.: Regressionnaya model diagnostiki patologiy molochnykh zhelez po dannym mikrovolnovoy radiotermometrii [regression model for diagnosis of breast pathology according to microwaves radiometry data]. Vestnik Volgogradskogo gosudarstvennogo universiteta. Seriya 1. Mathematica. Physica [Sci. J. Volgograd State Uni. Math. Phy.] 6(31), 72–82 (2015)

    Google Scholar 

  9. Bondar, S.S., Terekhov, I.V., Voevodin, A.A., Leonov, B.I., Khadartsev, A.A.: Assessment of transcapillary water exchange in the lungs by active radiometry. Biomed. Eng. 51(3), 211–214 (2017)

    Article  Google Scholar 

  10. Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: Lechevallier, Y., Saporta, G. (eds.) Proceedings of COMPSTAT 2010, pp. 177–186. Physica-Verlag, Heidelberg (2010). https://doi.org/10.1007/978-3-7908-2604-3_16

    Chapter  Google Scholar 

  11. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  12. Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Disc. 2, 121–167 (1998)

    Article  Google Scholar 

  13. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority oversampling technique. J. Artif. Int. Res. 16(1), 321–357 (2002)

    MATH  Google Scholar 

  14. Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 785–794. ACM, New York (2016)

    Google Scholar 

  15. Chollet, F.: Xception: Deep learning with depthwise separable convolutions. CoRR, abs/1610.02357 (2016)

    Google Scholar 

  16. Chollet, F., et al.: Keras (2015). https://keras.io

  17. Cireşan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Mitosis detection in breast cancer histology images with deep neural networks. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 411–418. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40763-5_51

    Chapter  Google Scholar 

  18. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    MATH  Google Scholar 

  19. Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theor. 13(1), 21–27 (2006)

    Article  MATH  Google Scholar 

  20. Crandall, J.P., et al.: Measurement of brown adipose tissue activity using microwave radiometry and 18F-FDG PET/CT. J. Nucl. Med. 59(8), 1243–1248 (2018)

    Article  Google Scholar 

  21. de Boer, P.-T., Kroese, D.P., Mannor, S., Rubinstein, R.Y.: A tutorial on the cross-entropy method. Ann. Oper. Res. 134(1), 19–67 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  22. Drakopoulou, M., Moldovan, C., Toutouzas, K., Tousoulis, D.: The role of microwave radiometry in carotid artery disease diagnostic and clinical prospective. Curr. Opin. Pharmacol. 39, 99–104 (2018)

    Article  Google Scholar 

  23. Fahlman, S.E., Lebiere, C.: The cascade-correlation learning architecture. In: Advances in Neural Information Processing Systems 2, pp. 524–532. Morgan Kaufmann Publishers Inc., San Francisco (1990)

    Google Scholar 

  24. Gabriel, S., Lau, R.W., Gabriel, C.: The dielectric properties of biological tissues: II. Measurements in the frequency range 10 Hz to 20 GHz. Phys. Med. Biol. 41(11), 2251 (1996)

    Article  Google Scholar 

  25. Gabriel, S., Lau, R.W., Gabriel, C.: The dielectric properties of biological tissues: III. Parametric models for the dielectric spectrum of tissues. Phys. Med. Biol. 41(11), 2271 (1996)

    Article  Google Scholar 

  26. Galazis, C., Vesnin, S., Goryanin, I.: Application of artificial intelligence in microwave radiometry (MWR). In: Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies, vol. 3, pp. 112–122 (2019). https://doi.org/10.5220/0007567901120122

  27. Gautherie, M.: Thermopathology of breast cancer: measurement and analysis of in vivo temperature and blood flow. Ann. N. Y. Acad. Sci. 335(1), 383–415 (1980)

    Article  Google Scholar 

  28. Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Teh, Y.W., Titterington, M. (eds.) Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 9, pp. 249–256. PMLR, Sardinia (2010)

    Google Scholar 

  29. Han, H., Wang, W.-Y., Mao, B.-H.: Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3644, pp. 878–887. Springer, Heidelberg (2005). https://doi.org/10.1007/11538059_91

    Chapter  Google Scholar 

  30. He, H., Bai, Y., Garcia, E.A., Li, S.: ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 1322–1328 (2008)

    Google Scholar 

  31. He, H., Ma, Y.: Imbalanced Learning: Foundations, Algorithms, and Applications, 1st edn. Wiley-IEEE Press (2013)

    Google Scholar 

  32. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. CoRR, abs/1502.03167 (2015)

    Google Scholar 

  33. Ivanov, Y., et al.: Use of microwave radiometry to monitor thermal denaturation of albumin. Front. Physiol. 9, 956 (2018)

    Article  Google Scholar 

  34. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR, abs/1412.6980 (2014)

    Google Scholar 

  35. Kim, B., et al.: Interpretability beyond feature attribution: quantitative testing with concept activation vectors (TCAV). In: International Conference on Machine Learning (2018)

    Google Scholar 

  36. Kobrinskii, B.A.: Konsul’tativnye intellektual’nye medicinskie sistemy: klassifikaciya, principy postroeniya, effektivnost’ [Advisory intelligent medical systems: classification, principles of construction, efficiency]. Vrach i informacionnye tekhnologii [Inf. Technol. Phys.] 2, 38–47 (2008)

    Google Scholar 

  37. Krawczyk, B.: Learning from imbalanced data: open challenges and future directions. Prog. Artif. Intell. 5(4), 221–232 (2016)

    Article  Google Scholar 

  38. Kubat, M., Matwin, S.: Addressing the curse of imbalanced training sets: one-sided selection. In: Proceedings of the Fourteenth International Conference on Machine Learning, pp. 179–186. Morgan Kaufmann (1997)

    Google Scholar 

  39. Laskari, K., Pitsilka, D., Pentazos, G., Siores, E., Tektonidou, M., Sfikakis, P.: SAT0657 microwave radiometry-derived thermal changes of sacroiliac joints as a biomarker of sacroiliitis in patients with spondyloarthropathy. Ann. Rheum. Dis. 77(Suppl. 2), 1178 (2018)

    Google Scholar 

  40. Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  41. Lim, T.-S., Loh, W.-Y., Shih, Y.-S.: A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Mach. Learn. 40(3), 203–228 (2000)

    Article  MATH  Google Scholar 

  42. Lin, M., Chen, Q., Yan, S.: Network in network. CoRR, abs/1312.4400 (2013)

    Google Scholar 

  43. Losev, A.G., Mazepa, E.A., Suleymanova, Kh.M.: O vzaimosvyazi nekotorykh priznakov RTM-diagnostiki zabolevaniy molochnykh zhelez [on interrelation of some signs of RTM diagnostics of mammary glands deseases]. Vestnik Volgogradskogo gosudarstvennogo universiteta. Seriya 1. Mathematica. Physica [Sci. J. Volgograd State Univ. Math. Phy.] 4(229), 35–44 (2015)

    Google Scholar 

  44. Losev, A.G., Levshinskii, V.V.: Intellektual’nyj analiz dannyh mikrovolnovoj radiotermometrii v diagnostike raka molochnoj zhelezy [Data mining of microwave radiometry data in the diagnosis of breast cancer]. Matematicheskaya fizika i komp’yuternoe modelirovanie [Math. Phys. Comput. Simul.] 20(5), 49–62 (2017)

    Google Scholar 

  45. Lundberg, S., Lee, S.-I.: A unified approach to interpreting model predictions. CoRR, abs/1705.07874 (2017)

    Google Scholar 

  46. Mazepa, E.A., Suleymanova, Kh.M.: Ob optimizacii chisla diagnosticheskih priznakov zabolevanii molochnih jelez na osnove termometricheskih dannih [On optimization of the number of diagnostic signs for breast diseases through thermometric data]. Vestnik Volgogradskogo gosudarstvennogo universiteta. Seriya 1. Mathematica. Physica [Sci. J. Volgograd State Univ. Math. Phys.] 6(37), 128–140 (2016)

    Google Scholar 

  47. Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on International Conference on Machine Learning, ICML 2010, pp. 807–814. Omnipress, USA (2010)

    Google Scholar 

  48. Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  49. Pentazos, G., Laskari, K., Prekas, K., Raftakis, J., Sfikakis, P., Siores, E.: Microwave radiometry-derived thermal changes of small joints as additional potential biomarker in rheumatoid arthritis: a prospective pilot study. J. Clin. Rheumatol. 24(1), 259–263 (2018)

    Google Scholar 

  50. Polyakov, M.V., Khoperskov, A.V., Zamechnic, T.V.: Numerical modeling of the internal temperature in the mammary gland. In: Siuly, S., et al. (eds.) HIS 2017. LNCS, vol. 10594, pp. 128–135. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69182-4_14

    Chapter  Google Scholar 

  51. Ribeiro, M.T., Singh, S., Guestrin, C.: “Why Should I Trust You?”: explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2016), pp. 1135–1144. ACM, New York (2016). https://doi.org/10.1145/2939672.2939778

  52. Rodrigues, D.B., et al.: Numerical 3D modeling of heat transfer in human tissues for microwave radiometry monitoring of brown fat metabolism. In: Proceeding of SPIE, vol. 8584 (2013)

    Google Scholar 

  53. Rodrigues, D.B., Stauffer, P.R., Pereira, P.J.S., Maccarini, P.F.: Microwave radiometry for noninvasive monitoring of brain temperature. In: Crocco, L., Karanasiou, I., James, M.L., Conceição, R.C. (eds.) Emerging Electromagnetic Technologies for Brain Diseases Diagnostics, Monitoring and Therapy, pp. 87–127. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75007-1_5

    Chapter  Google Scholar 

  54. Saniei, E., Setayeshi, S., Akbari, M.E., Navid, M.: Parameter estimation of breast tumour using dynamic neural network from thermal pattern. J. Adv. Res. 7(6), 1045–1055 (2016)

    Article  Google Scholar 

  55. Schneider, B.P., Miller, K.D.: Angiogenesis of breast cancer. J. Clin. Oncol. 23(8), 1782–1790 (2005). PMID: 15755986

    Article  Google Scholar 

  56. Schönberger, D.: Artificial intelligence in healthcare: a critical analysis of the legal and ethical implications. Int. J. Law Inf. Technol. 27(2), 171–203 (2019). https://doi.org/10.1093/ijlit/eaz004

    Article  Google Scholar 

  57. Semenov, S.: Microwave tomography: review of the progress towards clinical applications. Philos. Trans. Math. Phys. Eng. Sci. 367(1900), 3021–3042 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  58. Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: Breast cancer histopathological image classification using convolutional neural networks. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 2560–2567 (2016)

    Google Scholar 

  59. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  60. Tompson, J., Goroshin, R., Jain, A., LeCun, Y., Bregler, C.: Efficient object localization using convolutional networks. CoRR, abs/1411.4280 (2014)

    Google Scholar 

  61. Wilkins, M.F., Boddy, L., Morris, C.W., Jonker, R.: A comparison of some neural and non-neural methods for identification of phytoplankton from flow cytomery data. Bioinformatics 12(1), 9–18 (1996)

    Article  Google Scholar 

  62. Zamechnik, T.V., Larin, S.I., Losev, A.G.: Kombinirovannaya radiotermometriya kak metod issledovaniya venoznogo krovoobrascheniya nijnih konechnostei [Combined radiothermometry as a method for the study of venous circulation of the lower extremities] Volgograd. 252 p. (2015)

    Google Scholar 

  63. Zamechnik, T.V., Mazepa, E.A., Cherkesova, S.I., Pankova, J.V.: K voprosu ob optimizacii skriningovogo obsledovaniya molochnih jelez metodom mikrovolnovoi radiotermometrii [About the optimization of breast screening by means of microwave radiothermometry]. J. New Med. Technol. 21(4), 34–38 (2014)

    Google Scholar 

  64. Zenovich, A.V., Glazunov, V.A., Oparin, A.S., Primachenko, F.G.: Algoritmy prinyatiya resheniy v konsultativnoy intellektualnoy sisteme diagnostiki molochnykh zhelez [Algorithms of decision-making in the advisory intellectual system of diagnostics of mammary glands]. Math. Phys. Comput. Model. 6, 129–142 (2016)

    Google Scholar 

  65. Zenovich, A.V., Grebnev, V.I., Primachenko, F.G.: Algoritmy klassifikacii zabolevanij parnyh organov na osnove nejrosetej i nechetkih mnozhestv [Algorithms for the classification of diseases of paired organs on the basis of neural networks and fuzzy sets]. Matematicheskaya fizika i komp’yuternoe modelirovanie [Math. Phys. Comput. Simul.] 20(6), 26–37 (2017)

    Google Scholar 

  66. Vesnin, S., Turnbull, A.K., Dixon, J.M., Goryanin, I.: Modern microwave thermometry for breast cancer. J. Mol. Imaging Dyn. 7(2) (2017). https://doi.org/10.4172/2155-9937.1000136

  67. Zadeh, H.G., Montazeri, A., Kazerouni, I.A., Haddadnia, J.: Clustering and screening for breast cancer on thermal images using a combination of SOM and MLP. Comput. Methods Biomech. Biomed. Eng. Imaging Vis. 5(1), 68–76 (2017)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Volgograd State University, Volgograd, Russia

    Vladislav Levshinskii & Alexander Losev

  2. University of Edinburgh, Edinburgh, UK

    Christoforos Galazis & Igor Goryanin

  3. Medical Microwave Radiometry Ltd., Edinburgh, UK

    Lev Ovchinnikov & Sergey Vesnin

  4. Okinawa Institute of Science and Technology, Okinawa, Japan

    Lev Ovchinnikov & Igor Goryanin

Authors
  1. Vladislav Levshinskii
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christoforos Galazis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lev Ovchinnikov
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sergey Vesnin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alexander Losev
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Igor Goryanin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Igor Goryanin .

Editor information

Editors and Affiliations

  1. Universidade Nova de Lisboa, Caparica, Portugal

    Ana Roque

  2. Lodz University of Technology, Łódź, Poland

    Arkadiusz Tomczyk

  3. Nice Sophia Antipolis University, Nice Cedex 2, France

    Elisabetta De Maria

  4. University of Bremen, Bremen, Germany

    Felix Putze

  5. University of West Bohemia, Pilsen, Czech Republic

    Roman Moucek

  6. Instituto de Telecomunicações, University of Lisbon, Lisbon, Portugal

    Ana Fred

  7. Universidade Nova de Lisboa, Caparica, Portugal

    Hugo Gamboa

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

Levshinskii, V., Galazis, C., Ovchinnikov, L., Vesnin, S., Losev, A., Goryanin, I. (2020). Application of Data Mining and Machine Learning in Microwave Radiometry (MWR). In: Roque, A., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2019. Communications in Computer and Information Science, vol 1211. Springer, Cham. https://doi.org/10.1007/978-3-030-46970-2_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-46970-2_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-46969-6

  • Online ISBN: 978-3-030-46970-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation