Lung Cancer Detection by an Electronic Nose
- 1 Part 1: project profile
- 1.1 Project name
- 1.2 Project short description
- 1.3 Dates
- 1.4 Website(s)
- 1.5 People involved
- 1.6 Laboratory work and risk analysis
- 2 Part 2: project description
- 2.1 State of the art
- 2.2 Preliminary and sketches
- 2.3 Design notes and guidelines
- 2.4 Link to project documents and files
- 2.5 Description and results of experiments
- 2.6 Photos and videos
- 2.7 Link to source code of the software written for the project
- 2.8 Description and results of experiments
- 2.9 Useful internet links
Part 1: project profile
Lung Cancer Detection by an Electronic Nose
Project short description
The electronic nose is an instrument able to detect and recognize odors, that is the volatile substances in the atmosphere or emitted by the analyzed substance. This device can react to a gas substance by providing signals that can be analyzed to classify the input. It is composed of a sensor array (6 MOS sensors, in our case) and a pattern classification process based on machine learning techniques. In this project, we have been using an electronic nose based on an array of six MOS sensors, to recognize the presence of lung cancer in breaths' subjects, diagnosing the disease with a non invasive and low cost method.
During the first phase of our research, we have evaluated the possibility and accuracy of lung cancer diagnosis by classifying the olfactory signal associated to exhalations of subjects.
At the end of the first phase, results have been very satisfactory and promising: we achieved an average accuracy of 92.6%, sensitivity of 95.3% and specificity of 90.5%. In particular we analyzed the breath of 101 individuals, of which 58 control subjects, and 43 suffer 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’ at best, and to reduce the dimensionality of the problem, we have extracted the most significant features and projected them into a lower dimensional space using Non Parametric Linear Discriminant Analysis. Finally, we have used these features as input to several supervised pattern classification algorithms, based on different k-nearest neighbors (k-NN) approaches (classic, modified and Fuzzy k-NN), linear and quadratic discriminant classifiers and on a feed-forward artificial neural network (ANN). The observed results have all been validated using cross-validation.
These results pushed us to begin the second phase of the project, still in progress, to investigate the possibility of early lung cancer diagnosis: we are involving a larger number of subjects, partioned in different classes according to the type and stage of the disease. The research demonstrates that the electronic nose is a promising alternative to current lung cancer diagnostic techniques: the obtained predictive errors are lower than those achieved by present diagnostic methods, and the cost of the analysis, both in money, time and resources, is lower. The introduction of this technology will lead to very important social and business effects: its low price and small dimensions allow a large scale distribution, giving the opportunity to perform non invasive, cheap, quick, and massive early diagnosis and screening.
Start date: 2007/01/01
End date: --
At the moment no website avaible
A. Bonarini - User:AndreaBonarini
M. Matteucci - User:MatteoMatteucci
Other Politecnico di Milano people
R. Blatt - User:RossellaBlatt
Students currently working on the project
Claudio Trameri - User:ClaudioTrameri
Mauro Verdirosa - User:MauroVerdirosa
Students who worked on the project in the past
Dott. Ugo Pastorino (Istituto dei Tumori - Milano)
Dott. Elisa Calabrò (Istituto dei Tumori - Milano)
Dott. Matteo Della Torre (SACMI - Imola)
Laboratory work and risk analysis
Laboratory work for this project will be mainly performed at the Istituto Nazionale dei Tumori di Milano, where the acquisistion of subjects' breath, both sick and healthy will be done. For this kind of work, there are not potential risks.
Part 2: project description
State of the art
Preliminary and sketches
Design notes and guidelines
Link to project documents and files
Results obtained from this work have been presented at different conferences:
- Prestigious Applications of Intelligent Systems (PAIS 2008), Patras, Greece
- The 5th Prestigious Applications of Intelligent Systems (PAIS 2008) is a sub-conference of the 18th European Conference on Artificial Intteligence (ECAI 2008) that will be held at the University of Patras, Greece, from July 21st to 25th.
- International Joint Conference on Neural Networks (IJCNN 2007), Orlando, FL, USA
- Lung Cancer Identification by an Electronic Nose based on array of MOS Sensors, Blatt Rossella, Bonarini Andrea, Calabrò Elisa, Della Torre Matteo, Matteucci Matteo, Pastorino Ugo. Proceedings of the 2007 International Joint Conference on Neural Networks (IJCNN 2007), Orlando, FL, USA: File:IJCNNfinal.pdf
- Short presentation of the Lung Cancer Identification by an Electronic Nose based on an array of MOS Sensors paper: File:LungCancerIdentificationIJCNN2007.pdf
- International Workshop on Fuzzy Logic and Applications (WILF 2007), Ruta di Camogli, Genova, Italy
- Fuzzy k-NN Lung Cancer Identification by an Electronic Nose, Blatt Rossella, Bonarini Andrea, Calabrò Elisa, Della Torre Matteo, Matteucci Matteo, Pastorino Ugo. Proceedings of the 7th International Workshop on Fuzzy Logic and Applications, WILF 2007, Lecture Notes in Computer Science (LNAI), LNAI 4578, pages 261-268, Springer. Camogli (GE), Italy, July 2007.