Abstract
Colour, shape, geometry, contrast, irregularity and roughness of the visual appearance of vocal cords are the main visual features used by a physician to diagnose laryngeal diseases. This type of examination is rather subjective and to a great extent depends on physician’s experience. A decision support system for automated analysis of vocal cord images, created exploiting numerous vocal cord images can be a valuable tool enabling increased reliability of the analysis, and decreased intra- and inter-observer variability. This paper is concerned with such a system for analysis of vocal cord images. Colour, texture, and geometrical features are used to extract relevant information. A committee of artificial neural networks is then employed for performing the categorization of vocal cord images into healthy, diffuse, and nodular classes. A correct classification rate of over 93% was obtained when testing the system on 785 vocal cord images.
We gratefully acknowledge the support we have received from the Lithuanian State Science and Studies Foundation.
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Verikas, A., Gelzinis, A., Bacauskiene, M., Uloza, V. (2005). Intelligent Vocal Cord Image Analysis for Categorizing Laryngeal Diseases. In: Ali, M., Esposito, F. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2005. Lecture Notes in Computer Science(), vol 3533. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11504894_11
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DOI: https://doi.org/10.1007/11504894_11
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