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A New Method for Diagnosis of Cirrhosis Disease: Complex-valued Artificial Neural Network

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Abstract

In this study, complex-valued artificial neural network (CVANN) that is a new technique for biomedical pattern classification was proposed for classifying portal vein Doppler signals recorded from 54 patients with cirrhosis and 36 healthy subjects. Fast Fourier transform values of Doppler signals were calculated for pre-processing and obtained values, which include real and imaginary components, were used as the inputs of the CVANN for classification of Doppler signals. Classification results of CVANN show that Doppler signals were classified successfully with 100% correct classification rate using leave-one-out cross-validation. Besides, CVANN has 100% sensitivity and 100% specificity. These results were found to be compliant with the expected results that are derived from physician’s direct diagnosis. This method would be to assist the physician to make the final decision.

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Acknowledgments

I would like to acknowledge to Prof. Dr. Sadık Kara for his kind contribution in supplying the Doppler signals used in the experiments.

This work is supported by the Coordinatorship of Selçuk University’s Scientific Research Projects.

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Authors and Affiliations

  1. Engineering and Architecture Faculty, Department of Electrical and Electronics Engineering, Selcuk University, 42075, Konya, Turkey

    Yüksel Özbay

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  1. Yüksel Özbay
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Correspondence to Yüksel Özbay.

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Özbay, Y. A New Method for Diagnosis of Cirrhosis Disease: Complex-valued Artificial Neural Network. J Med Syst 32, 369–377 (2008). https://doi.org/10.1007/s10916-008-9142-z

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  • DOI: https://doi.org/10.1007/s10916-008-9142-z

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