Abstract
This paper illustrates the use of combined neural network (CNN) models to guide model selection for diagnosis of internal carotid arterial (ICA) disorders. The ICA Doppler signals were decomposed into time-frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The first level networks were implemented for the diagnosis of ICA disorders using the statistical features as inputs. To improve diagnostic accuracy, the second level network was trained using the outputs of the first level networks as input data. The CNN models achieved accuracy rates which were higher than that of the stand-alone neural network models.
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Acknowledgements
This study has been supported by Scientific Research Project of TOBB Economics and Technology University (Project No: ETU-BAP-2006/06).
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Übeyli, E.D. Combining Neural Network Models for Automated Diagnostic Systems. J Med Syst 30, 483–488 (2006). https://doi.org/10.1007/s10916-006-9034-z
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DOI: https://doi.org/10.1007/s10916-006-9034-z