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Neural network based approach for anomaly detection in the lungs region by electrical impedance tomography
Published in
2005
PMID: 15886443
Volume: 26
   
Issue: 4
Pages: 489 - 502
Abstract
In this paper, we have shown a simple procedure to detect anomalies in the lungs region by electrical impedance tomography. The main aim of the present study is to investigate the possibility of anomaly detection by using neural networks. Radial basis function neural networks are used as classifiers to classify the anomaly as belonging to the anterior or posterior region of the left lung or the right lung. The neural networks are trained and tested with the simulated data obtained by solving the mathematical model equation governing current flow through the simulated thoracic region. The equation solution and model simulation are done with FEMLAB. The effect of adding a higher number of neurons to the hidden layer can be clearly seen by the reduction in classification error. The study shows that there is interaction between the size (radius) and conductivity of anomalies and for some combination of these two factors the classification error of neural networks will be very small. © 2005 IOP Publishing Ltd.
About the journal
JournalPhysiological Measurement
ISSN09673334
Open AccessNo
Concepts (22)
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    ANALYTICAL ERROR
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    Article
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    Artificial neural network
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    COMPUTER ASSISTED IMPEDANCE TOMOGRAPHY
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    Computer simulation
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    Diagnostic imaging
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    Finite element analysis
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    LUNG BLOOD FLOW
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    LUNG MALFORMATION
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    Mathematical model
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    NON INVASIVE MEASUREMENT
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    Priority journal
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    THORACIC CAVITY
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    Diagnosis, computer-assisted
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    Electric impedance
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    Humans
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    Lung
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    LUNG DISEASES
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    Models, biological
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    Neural networks (computer)
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    Pattern recognition, automated
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    PLETHYSMOGRAPHY, IMPEDANCE