Hybrid Fuzzy CNN Network in the Problem of Medical Images Classification and Diagnostics

  • Yuriy ZaychenkoEmail author
  • Galib Hamidov
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1074)


The problem of classification of breast tumors on medical images is considered. For its solution new class of convolutional neural networks-hybrid CNN-FNN network is developed in which convolutional neural network VGG-16 is used as feature extractor while fuzzy neural network NEFClass is used as classifier. Training algorithms of FNN were implemented. The experimental investigations of the suggested hybrid network on the standard data set were carried out and comparison with known results was performed. The problem of data dimensionality reduction is considered and application of PCM method is investigated.


Breast tumor classification FNN NEFclass hybrid CNN 


  1. 1.
    Mohan, G., Subashini, M.M.: MRI based medical image analysis: survey on brain tumor grade classification. Biomed. Sig. Process. Control 39, 139–161 (2018)CrossRefGoogle Scholar
  2. 2.
    Ker, J., Wang, L.P., Rao, J., Lim, T.: Deep learning applications in medical image analysis. IEEE Access 6, 9375–9389 (2017)Google Scholar
  3. 3.
    Boyle, P., Levin, B. (eds.) World Cancer Report 2012. IARC, Lyon (2012).
  4. 4.
    Lakhani, S.R., Ellis, I.O., Schnitt, S., Tan, P., van de Vijver, M.: WHO classification of tumors of the breast, 4th edn. WHO Press, Lyon (2012)Google Scholar
  5. 5.
    Zhang, Y., Zhang, B., Coenen, F., Lu, W.: Breast cancer diagnosis from biopsy images with highly reliable random subspace classifier ensembles. Mach. Vis. Appl. 24(7), 1405–1420 (2013)CrossRefGoogle Scholar
  6. 6.
    Zhang, Y., Zhang, B., Coenen, F., Xiau, J., Lu, W.: One-class kernel subspace ensemble for medical image classification. EURASIP J. Adv. Sig. Process. 2014(17), 1–13 (2014)Google Scholar
  7. 7.
    Doyle, S., Agner, S., Madabhushi, A., Feldman, M., Tomaszewski, J.: Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features. In: Proceedings of the 5th IEEE International Symposium on Biomedical Imaging (ISBI): From Nano to Macro, vol. 61, pp. 496–499. IEEE, May 2008Google Scholar
  8. 8.
    Singh, A., Mansourifar, H., Bilgrami, H., Makkar, N., Shah, T.: Classifying Biological Images Using Pre-trained CNNs.
  9. 9.
    Spanhol, F., Oliveira, L.S., Petitjean, C., Heutte, L.: A dataset for breast cancer histopathological image classification. IEEE Trans. Biomed. Eng. 63, 1455–1462 (2016)Google Scholar
  10. 10.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)CrossRefGoogle Scholar
  11. 11.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Image net classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105 (2012)Google Scholar
  12. 12.
    Olson, B., Hashmi, I., Molloy, K., Shehu, A.: Basin hopping as a general and versatile optimization framework for the characterization of biological macromolecules. Adv. Artif. Intell. 2012 (2012). Article ID 674832Google Scholar
  13. 13.
    Nauck, D., Kruse, R.: New learning strategies for NEFCLASS. In: Proceedings of Seventh International Fuzzy Systems Association World Congress, IFSA 1997, vol. IV, pp. 50–55. Academia Prague (1997)Google Scholar
  14. 14.
    Zaychenko, Y.P., Sevaee, F., Matsak, A.V.: Fuzzy neural networks for economic data classification. Vestnik Natl. Tech. Univ. Ukraine “KPI” Sect. “Informatic, Control Comput. Eng. 42, 121–133 (2004)Google Scholar
  15. 15.
    Zaychenko, Y.P., Petrosyuk I.M., Jaroshenko, M.S.: The investigations of fuzzy neural networks in the problems of electro-optical images recognition. Syst. Res. Inf. Technol. (4), 61–76 (2009)Google Scholar
  16. 16.
    Zaychenko, Y., Huskova, V.: Recognition of objects on optical images in medical diagnostics using fuzzy neural network NEFClass. Int. J. Inf. Models Anal. 4(1), 13–22 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute for Applied System Analysis, Igor Sikorsky Kyiv Polytechnic KyivKievUkraine
  2. 2.Information Technologies Department, AzershigBakuAzerbaijan

Personalised recommendations