ENT Bacteria classification using a neural network based Cyranose 320 electronic nose
UNSPECIFIED (2004) ENT Bacteria classification using a neural network based Cyranose 320 electronic nose. In: IEEE Sensors 2004 Conference, OCT 24-27, 2004, Vienna Univ Technol, Vienna, AUSTRIA.Full text not available from this repository.
An electronic nose (e-nose), the Cyrano Sciences' Cyranose 320 (see Fig. I), comprising an array of thirty-two polymer carbon black composite sensors has been used to identify 3 species of bacteria responsible for ear nose and throat (ENT) infections when present in standard agar solution. Swab samples were collected from the infected areas of the ENT patients' ear, nose and throat regions. Gathered data were a very complex mixture of different chemical compounds. An innovative data clustering approach was investigated for these bacteria data by combining the Principal Component Analysis (PCA) based 3-dimensional scatter plot, Fuzzy C Means (FCM) and Self Organizing Map (SOM) network. Using these three data clustering algorithms simultaneously better 'classification' of three ENT bacteria classes were represented. Then three supervised classifiers, namely Multi Layer Perceptron (MLP), Probabilistic Neural network (PNN) and Radial basis function network (RBF), were used to classify the three bacteria classes. A comparative evaluation of the classifiers was conducted for this application.
|Item Type:||Conference Item (UNSPECIFIED)|
|Subjects:||T Technology > TK Electrical engineering. Electronics Nuclear engineering|
|Series Name:||IEEE Sensors|
|Journal or Publication Title:||PROCEEDINGS OF THE IEEE SENSORS 2004, VOLS 1-3|
|Editor:||Rocha, D and Sarro, PM and Vellekoop, MJ|
|Number of Pages:||2|
|Page Range:||pp. 324-325|
|Title of Event:||IEEE Sensors 2004 Conference|
|Location of Event:||Vienna Univ Technol, Vienna, AUSTRIA|
|Date(s) of Event:||OCT 24-27, 2004|
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