Lung Cancer Identification by an Electronic Nose based on an Array of MOS Sensors

We present a method to recognize the presence of lung cancer in individuals by classifying the olfactory signal acquired through an electronic nose based on an array of MOS sensors. We analyzed the breath of 101 persons, of which 58 as control and 43 suffering from different types of lung cancer (primary and not) at different stages. In order to find the components able to discriminate between the two classes 'healthy' and 'sick' as best as possible and to reduce the dimensionality of the problem, we extracted the most significative features and projected them into a lower dimensional space, using Non Parametric Linear Discriminant Analysis. Finally, we used these features as input to several supervised pattern classification techniques, based on different k-nearest neighbors (k-NN) approaches (classic, modified and Fuzzy k-NN), linear and quadratic discriminant classifiers and on a feedforward artificial neural network (ANN). The observed results, all validated using cross-validation, have been satisfactory, achieving an accuracy of 92.6%, a sensitivity of 95.3% and a specificity of 90.5%. These results put the electronic nose as a valid implementation of lung cancer diagnostic technique, being able to obtain excellent results with a non invasive, small, low cost and very fast instrument.