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Classification of ear, nose, and throat bacteria using a neural-network-based electronic nose
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UNSPECIFIED (2004) Classification of ear, nose, and throat bacteria using a neural-network-based electronic nose. MRS BULLETIN, 29 (10). pp. 709-713. ISSN 0883-7694
Full text not available from this repository.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.
| Item Type: | Journal Article |
|---|---|
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) Q Science > QC Physics |
| Journal or Publication Title: | MRS BULLETIN |
| Publisher: | MATERIALS RESEARCH SOCIETY |
| ISSN: | 0883-7694 |
| Date: | October 2004 |
| Volume: | 29 |
| Number: | 10 |
| Number of Pages: | 5 |
| Page Range: | pp. 709-713 |
| Publication Status: | Published |
| URI: | http://wrap.warwick.ac.uk/id/eprint/7878 |
Data sourced from Thomson Reuters' Web of Knowledge
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