Keele Research Repository

Lung cancer is a widespread disease and it is well understood that systematic, non-invasive and early detection of this progressive and life-threatening disorder is of vital importance for patient outcomes. In this work we present a convergence of familiar and less familiar artificial neural network techniques to help address this task. Our preliminary results demonstrate that improved, automated, early diagnosis of lung cancer based on the classification of volatile organic compounds detected in the exhaled gases of patients seems possible. Under strictly controlled conditions, using Selected Ion Flow Tube Mass Spectrometry (SIFT-MS), the naturally occurring concentrations of a range of volatile organic compounds in the exhaled gases of 20 lung cancer patients and 20 healthy individuals provided the dataset that has been analysed. We investigated the performance of several artificial neural network architectures, each with complementary pattern recognition properties, from the domains of supervised, unsupervised and recurrent neural networks. The neural networks were trained on a subset of the data, with their performance evaluated using unseen test data and classification accuracies ranging from 56% to 74% were obtained. In addition, there is promise that the topological ordering properties of the unsupervised networks’ clusters will be able to provide further diagnostic insights, for example into patients who may have been heavy smokers but so far have not presented with any lung cancer. With the collection of data from a larger number of subjects across a long time period there is promise that an automated assistive tool in the diagnosis of lung cancer via breath analysis could soon be possible.