Difference between revisions of "Publications:Categorizing laryngeal images for decision support"
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| − | |Name=Gelzinis, Adas (Department of Applied Electronics, Kaunas University of Technology, Lithuania);Verikas, Antanas | + | |Name=Gelzinis, Adas (Department of Applied Electronics, Kaunas University of Technology, Lithuania);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));Bacauskiene, Marija (Department of Applied Electronics, Kaunas University of Technology, Lithuania) |
|Title=Categorizing laryngeal images for decision support | |Title=Categorizing laryngeal images for decision support | ||
|PublicationType=Book Chapter | |PublicationType=Book Chapter | ||
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| Title | Categorizing laryngeal images for decision support |
|---|---|
| Author | |
| Year | 2007 |
| PublicationType | Book Chapter |
| Journal | |
| HostPublication | Advanced Concepts for Intelligent Vision Systems : 9th International Conference, ACIVS 2007, Delft, The Netherlands, August 28-31, 2007 ; proceedings |
| Conference | |
| DOI | http://dx.doi.org/10.1007/978-3-540-74607-2_47 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:239283 |
| Abstract | This paper is concerned with an approach to automated analysis of vocal fold images aiming to categorize laryngeal diseases. Colour, texture, and geometrical features are used to extract relevant information. A committee of support vector machines is then employed for performing the categorization of vocal fold images into healthy, diffuse, and nodular classes. The discrimination power of both, the original and the space obtained based on the kernel principal component analysis is investigated. A correct classification rate of over 92% was obtained when testing the system on 785 vocal fold images. Bearing in mind the high similarity of the decision classes, the correct classification rate obtained is rather encouraging. |
