Difference between revisions of "Publications:Intelligent vocal cord image analysis for categorizing laryngeal diseases"
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| − | |Name=Verikas, Antanas | + | |Name=Verikas, Antanas (av) (0000-0003-2185-8973) (Högskolan i Halmstad (2804), Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) (3905), Halmstad Embedded and Intelligent Systems Research (EIS) (3938));Gelzinis, Adas (Kaunas University of Technology, Lithuania);Bacauskiene, Marija (Kaunas University of Technology, Lithuania);Uloza, Virgilijus (Kaunas University of Medicine, Kaunas, Lithuania) |
|Title=Intelligent vocal cord image analysis for categorizing laryngeal diseases | |Title=Intelligent vocal cord image analysis for categorizing laryngeal diseases | ||
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Latest revision as of 22:42, 30 September 2016
| Title | Intelligent vocal cord image analysis for categorizing laryngeal diseases |
|---|---|
| Author | Antanas Verikas and Adas Gelzinis and Marija Bacauskiene and Virgilijus Uloza |
| Year | 2005 |
| PublicationType | Conference Paper |
| Journal | |
| HostPublication | Innovations in applied artificial intelligence |
| Conference | 18th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems IEA/AIE 2005 |
| DOI | http://dx.doi.org/10.1007/11504894_11 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:300233 |
| 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. |
