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1.	Verikas, Antanas, et al. (author) Intelligent vocal cord image analysis for categorizing laryngeal diseases 2005 In: Innovations in applied artificial intelligence. - Berlin, Heidelberg : Springer. - 9783540265511 ; , s. 69-78 Conference paper (peer-reviewed)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.

Verikas, Antanas, et al. (author)
Intelligent vocal cord image analysis for categorizing laryngeal diseases
2005
In: Innovations in applied artificial intelligence. - Berlin, Heidelberg : Springer. - 9783540265511 ; , s. 69-78
Conference paper (peer-reviewed)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.

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