Skip to main content

Classification of MCA Stenosis in Diabetes by MLP and RBF Neural Network

Journal of Medical Systems volume 28, pages 475–487 (2004)Cite this article

Abstract

For the classification of Middle Cerebral Artery (MCA) stenosis, Doppler signals have been received from the diabetes and control group by using 2 MHz Transcranial Doppler. After the Fast Fourier Transform (FFT) analyses of the Doppler signals, Power Spectrum Density (PSD) estimations have been made and Multilayer Perceptron (MLP) and Radial Basis Function (RBF) have been dealt to apply to the neural networks. PSD estimations of Doppler signals received from MCA of 104 subjects have been successfully classified by MLP (correct classification = 94.2%) and RBF (correct classification = 88.4%) neural network. As we have seen in the area under ROC curve (AUC), MLP neural network (AUC = 0.934) has classified more successfully when compared with RBF neural network (AUC = 0.873).

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

43,34 €

Price includes VAT (Finland)
Tax calculation will be finalised during checkout.

REFERENCES

  1. Aikimbaev, K., Guvenc, B., Canataroglu, A., Canataroglu, H., Baslamisli, F., and Oguz, M., Value of duplex and color Doppler ultrasonograpy in the evaluation of orbital vascular flow and resistance in sickle cell disease. Am.J.Hematol. 67:163–167, 2001.

    Google Scholar 

  2. Bluth, E. L., Stavros, A. T., Marich, K. W., Wetzner, S. M., Aufrichtig, D., and Baker, L. D., Carotid dublex sonography: A multicenter recommendation for standardized imaging and Doppler ultra-sonography criteria. Radiographics 8:487–506, 1988.

    Google Scholar 

  3. Evans, D. H., McDicken, W. N., Skidmore, R., and Woodcock, J. P., Doppler Ultrasound: Physics, Instrumentation and Clinical Applications, Wiley, Chicester, UK, 2000.

    Google Scholar 

  4. Newell, D. W., and Aaslid, R., Transcranial Doppler, Raven Press, New York, 1992.

    Google Scholar 

  5. Beale, R., and Jackson, T., Neural Computing: AnIntroduction, Institute of Physics Publishing, Bristol, UK, 1990.

    Google Scholar 

  6. Bruzzone, L., Prieto, D. F., and Serpico, S. B., A neural-statistical approach to multitemporal and multisource remote-sensing image classification. IEEE Trans.Geosci.Remote Sensing 30:1350–1359, 1999.

    Google Scholar 

  7. Hwang, J. N., Kung, S. Y., Niranjan, M., and Principe, J. C., The past, present, and future of neural networks for signal processing. IEEE Signal Process.Magn. 14:28–48, 1997.

    Google Scholar 

  8. Grudin, M. A., On internal representation in face recognition systems. Pattern Recognit. 33(6):1161–1177, 2000.

    Google Scholar 

  9. Yung, M. H., Kreigman, D. J., and Ahuja, N., Detecting face in images: Asurvey. IEEE Trans.Pattern Anal.Mach.Int. 34(1):34–58, 2002.

    Google Scholar 

  10. Haddadnia, L., and Faez, K., Humanface recognition based on shape information and pseudo Zernike moment. Proc.5th International Fall Workshop on Vision, Modeling and Visualization, Saarbrucken, Germany, November 22- 24, pp. 113–118, 2000.

  11. White, H., Learning in artificial neural networks: A statistical approach. Neural Comput. 1:425–464, 1989.

    Google Scholar 

  12. Baxt, W. G., Application of neural networks to clinical medicine. Lancet 346:1135–1138, 1995.

    Google Scholar 

  13. Mobley, B. A., Schecter, E., Moore, W. E., McKee, P. A., and Eichner, J. E., Predictions of coronary artery stenosis by artificial neural network. Artif.Intell.Med. 18:187–203, 2000.

    Google Scholar 

  14. Hara, Y., Atkins, R. G., Yueh, S. H., Shin, R. T., and Kong, J. A., Application on neural networks to radar image classification. IEEE Trans.Geo.Remote Sensing 32(1):100–109, 1994.

    Google Scholar 

  15. Bishop, C. M., Neural Network for Pattern Recognition, Oxford University Press, New York, 1995.

    Google Scholar 

  16. Bezerianos, A., Papadimitrious, S., and Alexopoulos, D., Radial basis function neural networks for the characterization of heart rate variability dynamics. Artif.Intell.Med. 15:215–234, 1999.

    Google Scholar 

  17. Zhou, W., Verification of the nonparametric characteristics of backpropagation neural networks for image classification. IEEE Trans.Geo.Remote Sensing 37(2):771–779, 1999.

    Google Scholar 

  18. Yingwei, L., Sundarajan, N., and Saratchandran, P., Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm. IEEE Trans.Neural Netw. 9(2):308–318, 1998.

    Google Scholar 

  19. Güler, İ, Hardalaç F., and Kaymaz, M., Comparison of FFT and Adaptive ARMA methods in Transcranial Doppler signals recorded from the cerebral vessels. Comput.Biol.Med. 32:445–453, 2002.

    Google Scholar 

  20. Güler, İ, Hardalaç F., and Müldür, S., Determination of aorta failure with the application of FFT, AR and wavelet methods to Doppler technique. Comput.Biol.Med. 31:229–238, 2001.

    Google Scholar 

  21. Hardalaç F., Serhatl?o?lu, S., and Güler, İ, Classification of Transcranial Doppler signals using arti-ficial neural network. Journal of Medical Systems 27(2):205–214, 2003.

    Google Scholar 

  22. Hardalaç F., Serhatl?o?lu, S., Burma, O., and Güler, İ, Determination of coronary failure with appli-cation of FFT and AR methods. Journal of Medical Systems 27(2):121–131, 2003.

    Google Scholar 

  23. Güler, I., Hardalaç F., and Erol, F. S., Comparison of FFT, AR and wavelet methods in transcranial Doppler signal obtained from intracerebral vessels. Proc.23rd IEEE/EMBS, ?stanbul, Turkey, 2001.

    Google Scholar 

  24. Powell, M. J. D., Radial basis functions for multivariable interpolation: a review. In Cox, M. G., and Mason, J. C. (eds.), Algorithms for the Approximation of Functions and Data, Oxford University Press, New York, 1985.

    Google Scholar 

  25. Broomhead, D. S., and Lowe, D., Multivariable functional interpolation and adaptive networks. Complex Syst. 2:321–355, 1988.

    Google Scholar 

  26. Moody, J., and Darken, C. J., Fast learning in networks of locally tuned processing units. Neural Comput. 1:281–294, 1989.

    Google Scholar 

  27. Hanley, J. A., and McNeil, B. J., The meaning and use of the area under the receiver operating characteristic (ROC) curve. Radiology 143:29–36, 1982.

    Google Scholar 

Download references

Author information

Affiliations

  1. Department of Electronic and Computer Education, Faculty of Technical Education, Gazi University, Ankara, Turkey

    Uçman Ergün, Necaattin Barýþçý & İnan Güler

  2. Department of Public Health, Faculty of Medicine, Fırat University, Elazıg, Turkey

    Ahmet Tevfik Ozan

  3. Department of Radiology, Faculty of Medicine, Fırat University, Elazıg, Turkey

    Selami Serhatlýoðlu & Erkin Oğur

  4. Department of Biophysics, Faculty of Medicine, Fırat University, Elazığ, Turkey

    Fırat Hardalaç

Authors
  1. Uçman Ergün
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Necaattin Barýþçý
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ahmet Tevfik Ozan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Selami Serhatlýoðlu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Erkin Oğur
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Fırat Hardalaç
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. İnan Güler
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Ergün, U., Barýþçý, N., Ozan, A.T. et al. Classification of MCA Stenosis in Diabetes by MLP and RBF Neural Network. Journal of Medical Systems 28, 475–487 (2004). https://doi.org/10.1023/B:JOMS.0000041174.34685.5b

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/B:JOMS.0000041174.34685.5b

  • MCA stenosis
  • transcranial doppler
  • PSD estimation
  • multilayer perceptron neural network
  • radial basis function neural network