Atul S Minhas and M Ramasubba Reddy 2005 Physiol. Meas. 26 489 doi:10.1088/0967-3334/26/4/014
Atul S Minhas and M Ramasubba Reddy
Show affiliationsIn 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.
87.63.Pn Electrical impedance tomography (EIT)
07.05.Mh Neural networks, fuzzy logic, artificial intelligence
Issue 4 (August 2005)
Received 16 September 2004, accepted for publication 8 April 2005
Published 10 May 2005
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