Abstract
The aim of this paper is to develop an automated diagnosis system for arterial diseases from the Doppler ultrasonography spectrogram. A feature-based classification of Doppler spectrogram has been done for its clinical assessment. Development of the automated diagnostic tool for Doppler spectrogram involves three steps (a) feature extraction, (b) classification of spectrogram based on clinical symptoms and (c) diagnosis of arterial condition of the concerned region. Artificial Neural Network is used for classification of the spectrograms. Arterial condition of a specified region is evaluated from symptoms obtained from a number of spectrograms in the different nearby regions. Bayesian probabilistic method is used for diagnostic evaluation of the arterial status from the obtained spectrogram. The results satisfied 83% of the obtained cases.
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© 2000 Springer-Verlag Berlin Heidelberg
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Das, B., Mitra, S.K., Banerjee, S. (2000). Knowledge Base System for Diagnostic Assessment of Doppler Spectrogram. In: Cairó, O., Sucar, L.E., Cantu, F.J. (eds) MICAI 2000: Advances in Artificial Intelligence. MICAI 2000. Lecture Notes in Computer Science(), vol 1793. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10720076_37
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DOI: https://doi.org/10.1007/10720076_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67354-5
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