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ENT Bacteria classification using a neural network based Cyranose 320 electronic nose

By R. Dutta, J. W. Gardner and Evor Hines

Abstract

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

Topics: TK
Publisher: IEEE
Year: 2005
OAI identifier: oai:wrap.warwick.ac.uk:34208
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