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Classification of the Frequency of Carotid Artery Stenosis with MLP and RBF Neural Networks in Patients with Coroner Artery Disease

Journal of Medical Systems volume 28, pages 591–601 (2004)Cite this article

Abstract

For the classification of left and right Internal Carotid Arteries (ICA) stenosis, Doppler signals have been received from the patients with coroner arteries stenosis by using 6.2–8.4 MHz linear transducer. To be able to classify the data obtained from LICA and RICA in artificial intelligence, MLP and RBF neural networks were used. The number of obstructed veins from the coroner angiography, intimal thickness, and plaque formation from the power Doppler US and resistive index values were used as the input data for the neural networks. Our findings demonstrated that 87.5% correct classification rate was obtained from MLP neural network and 80% correct classification rate was obtained from RBF neural network. MLP neural network has classified more successfully when compared with RBF neural network.

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REFERENCES

  1. Steinke, W., and Kloetzsch, C., Hennereci, Carotid artery disease assessed by color Doppler flowimag-ing: Correlation with standard Doppler sonography and angiography. Ann. J. Roentgenol. 154:1061–1068, 1990.

    Google Scholar 

  2. Wagner, W. H., Treiman, R. L., Cossman, D. V., et al., The diminishing role of diagnostic arteriography in carotid artery disease: Duplex scanning as definitive preoperative study. Ann. Vasc. Surg. 5:105–110, 1991.

    Google Scholar 

  3. Blossom, G. B., Fietsam, R. J., Bassets, J. S., et al., Characteristics of cerebrovascular accidents after coronary artery bypass grafting. Ann. Surg. 58:584–589, 1992.

    Google Scholar 

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

    Google Scholar 

  5. 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 

  6. 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. Mag. 14:28–48, 1997.

    Google Scholar 

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

    Google Scholar 

  8. 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 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  12. 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 

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

    Google Scholar 

  14. 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 

  15. 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 

  16. 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 

  17. Fredric, M. H., and Inica, K., Principles of Neurocomputing for Science and Engineering, McGraw-Hill, New York, 2001.

    Google Scholar 

  18. Ergün, U., Serhatlýo∂lu, S., Hardalaç F., and Güler Ý., Classification of carotid artery stenosis of the patients with diabetes by neural network and logistic regression. Comput. Biol. Med., 34:389–405, 2004.

    Google Scholar 

  19. McClelland, J. L., and Rumelhart, D. E., Explorations in Parallel Distributed Processing: AHandbook of Models, Programs and Exercises, MIT Press, Cambridge, MA, 1986.

    Google Scholar 

  20. 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 

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

    Google Scholar 

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

    Google Scholar 

  23. Zweig, M. H., and Campbell, G., Receiver-operating characteristic (ROC) plots: A fundamental evaluation tool in clinical medicine. Clin. Chem. 39:561–577, 1993.

    Google Scholar 

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Affiliations

  1. Department of Radiology, Faculty of Medicine, Fırat University, Elazig, Turkey

    Hanefi Yýldýrým, Hasan Baki Altýnsoy & Erkin Oğur

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

    Necaattin Barýpçý, Uçman Ergün & İnan Güler

  3. Department of Biophysics, Faculty of Medicine, Fırat University, Elazig, Turkey

    Fırat Hardalaç

Authors
  1. Hanefi Yýldýrým
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  2. Hasan Baki Altýnsoy
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  3. Necaattin Barýpçý
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  4. Uçman Ergün
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  5. Erkin Oğur
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  6. Fırat Hardalaç
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  7. İnan Güler
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Correspondence to Hanefi Yýldýrým.

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Yýldýrým, H., Altýnsoy, H.B., Barýpçý, N. et al. Classification of the Frequency of Carotid Artery Stenosis with MLP and RBF Neural Networks in Patients with Coroner Artery Disease. Journal of Medical Systems 28, 591–601 (2004). https://doi.org/10.1023/B:JOMS.0000044961.38008.97

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  • DOI: https://doi.org/10.1023/B:JOMS.0000044961.38008.97

  • carotid arteries stenosis
  • coroner arteries stenosis
  • Doppler ultrasonography
  • MLP neural network
  • RBF neural network