Publications:Exploiting image, voice, and patient's questionnaire for screening laryngeal disorders

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Title Exploiting image, voice, and patient's questionnaire for screening laryngeal disorders
Author Antanas Verikas and Virgilijus Uloza and Adas Gelzinis and Marija Bacauskiene and Ruta Pribuisiene and Marius Kaseta
Year 2009
PublicationType Conference Paper
Journal
HostPublication Proceedings of the 3rd Advanced Voice Function Assessement International Workshop (AVFA 2009)
Conference 3rd Advanced Voice Function Assessement International Workshop (AVFA 2009), May 18-20, 2009, Madrid, Spain
DOI
Diva url http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:236200
Abstract This paper is concerned with soft computing techniques for categorizing laryngeal disorders based on information extracted from an image of patient's vocal folds, a voice signal, and questionnaire data. Multiple feature sets are used to characterize images and voice signals. A committee of support vector machines (SVM) is designed for categorizing the data represented by the multiple feature sets into the healthy, nodular and diffuse classes. The feature selection and classifier design is combined into the same learning process based on genetic search. When testing the developed tools on the set of data collected from 240 patients, the classification accuracy of over 98.0% was obtained. Combination of the three modalities allowed to substantially improve the classification accuracy if compared to the highest accuracy obtained from a single modality.