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Classification of Ear, Nose, and Throat Bacteria Using a Neural-Network-Based Electronic Nose

Published online by Cambridge University Press:  31 January 2011

Ritaban Dutta,
Julian W. Gardner and
Evor L. Hines
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Abstract

This article describes the use of an electronic nose (the Cyranose 320) to sense and identify three species of bacteria responsible for ear, nose, and throat (ENT) infections. Gathered data were a complex mixture of different chemical compounds. An innovative approach for classifying the bacteria data was investigated by using a combination of several clustering algorithms. The best results suggest that the three classes of bacteria examined can be predicted with up to 98% accuracy, allowing more precise diagnosis of ENT infection in patients. This type of bacteria data analysis and feature extraction is difficult, but it can be concluded that combined use of the analysis methods described here can solve the feature extraction problem with very complex data and enhance the performance of electronic noses.

Keywords

bacteria analysiselectronic nosesENT infectionsfuzzy c-meansmultilayer perceptronprincipal component analysisprobabilistic neural networksradial basis function networksself-organizing map networks.

Information

Type
Research Article
Information
MRS Bulletin , Volume 29 , Issue 10: Novel Materials and Applications of Electronic Noses and Tongues , October 2004, pp. 709 - 713
Copyright
Copyright © Materials Research Society 2004

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References

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