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Authors: John C. Cancilla 1 ; Bin Wang 2 ; Pablo Diaz-Rodriguez 1 ; Gemma Matute 1 ; Hossam Haick 2 and Jose S. Torrecilla 1

Affiliations: 1 Complutense University of Madrid, Spain ; 2 Technion-Israel Institute of Technology, Israel

ISBN: 978-989-758-054-3

Keyword(s): Lung Cancer, Breath Biomarkers, SiNW FET Sensors, Neural Networks.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computer-Supported Education ; Domain Applications and Case Studies ; Enterprise Information Systems ; Fuzzy Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Industrial, Financial and Medical Applications ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: Cancer is currently one of deadliest and most feared diseases in the developed world, and, particularly, lung cancer (LC) is one of the most common types and has one of the highest death/incidence ratios. An early diagnosis for LC is probably the most accessible possibility to try and save patients and lower this ratio. Recently, research concerning LC-related breath biomarkers has provided optimistic results and has become a real option to try and obtain a fast, reliable, and early LC diagnosis. In this paper, a combination of field-effect transistor (FET) sensors and artificial neural networks (ANNs) has been employed to classify and estimate the partial pressures of a series of polar and nonpolar volatile organic compounds (VOCs) present in prepared gaseous mixtures. The objective of these preliminary tests is to give an idea of how well this technology can be used to analyze artificial or real breath samples by quantifying the LC-related VOCs or biomarkers. The results of this ste p are very promising and indicate that this methodology deserves further research using more complex samples to find the existing limitations of the FET-ANN combination. (More)

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Paper citation in several formats:
C. Cancilla, J.; Wang, B.; Diaz-Rodriguez, P.; Matute, G.; Haick, H. and Torrecilla, J. (2014). Volatile Organic Compound Detection with FET Sensors and Neural Network Data Processing as a Preliminary Step to Early Lung Cancer Diagnosis.In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014) ISBN 978-989-758-054-3, pages 56-64. DOI: 10.5220/0005068700560064

@conference{ncta14,
author={John C. Cancilla. and Bin Wang. and Pablo Diaz{-}Rodriguez. and Gemma Matute. and Hossam Haick. and Jose S. Torrecilla.},
title={Volatile Organic Compound Detection with FET Sensors and Neural Network Data Processing as a Preliminary Step to Early Lung Cancer Diagnosis},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)},
year={2014},
pages={56-64},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005068700560064},
isbn={978-989-758-054-3},
}

TY - CONF

JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)
TI - Volatile Organic Compound Detection with FET Sensors and Neural Network Data Processing as a Preliminary Step to Early Lung Cancer Diagnosis
SN - 978-989-758-054-3
AU - C. Cancilla, J.
AU - Wang, B.
AU - Diaz-Rodriguez, P.
AU - Matute, G.
AU - Haick, H.
AU - Torrecilla, J.
PY - 2014
SP - 56
EP - 64
DO - 10.5220/0005068700560064

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