Journal ArticleDOI
Neural network based approach for anomaly detection in the lungs region by electrical impedance tomography.
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TLDR
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.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.read more
Citations
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Proceedings ArticleDOI
Distributed online anomaly detection in high-content screening
Adam Goode,Rahul Sukthankar,Lily Mummert,Mei Chen,Jeffrey Saltzman,D. Ross,S. Szymanski,A. Tarachandani,Mahadev Satyanarayanan +8 more
TL;DR: The proposed approach employs assay-specific image processing within an assay-independent framework for distributed control, machine learning, and anomaly reporting, and exploits coarse-grained parallelism to distribute image processing over several computing nodes while efficiently aggregating sufficient statistics across nodes.
Journal ArticleDOI
On the influence of spread constant in radial basis networks for electrical impedance tomography.
TL;DR: A novel approach based on radial basis function (RBF) artificial neural networks (ANNs) to solve the EIT inverse problem and evidences a strong correlation between the area of the target and the spread constant of the RBF network that gives the best reconstruction.
Image Analysis for X-ray Imaging of Food
TL;DR: In this paper, a grating-based X-ray imaging and advanced analysis was used for microstructure analysis of protein microstructures, and a segmentation framework was presented, from which geometrical parameters were assessed.
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Deep Analysis of EIT Dataset to Classify Apnea and Non-Apnea Cases in Neonatal Patients
Nafiseh Vahabi,Rebecca J. Yerworth,Martijn Miedema,Anton H. van Kaam,Richard Bayford,Andreas Demosthenous +5 more
TL;DR: In this article, the authors presented the first use of EIT boundary voltage data obtained from infants for the automatic detection of apnea using machine learning, and investigated which components contain the main features of the apnea events, and applied a hybrid classification model that combines two established methods; a pre-trained transfer learning method with a convolutional neural network with 50 layers deep (ResNet50) architecture, and a support vector machine (SVM) classifier ResNet50 training was undertaken using an ImageNet dataset.
Journal ArticleDOI
Reconstruction convergence and speed enhancement in electrical impedance tomography for domains with known internal boundaries.
TL;DR: An improved approach for electrical impedance tomography (EIT) image reconstruction, based on modifying the forward and inverse solutions, is proposed, and its speed is 2-200 times higher than the previously developed methods with the same level of precision.
References
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Orthogonal least squares learning algorithm for radial basis function networks
TL;DR: The authors propose an alternative learning procedure based on the orthogonal least-squares method, which provides a simple and efficient means for fitting radial basis function networks.
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Electrical Impedance Tomography
TL;DR: A survey of the work in electrical impedance tomography can be found in this article, where the authors survey some of the most important works in the field. Butt.t.
Journal ArticleDOI
An electric current tomograph
TL;DR: A description is given of an instrument designed to acquire data for the construction of images of internal body structures based on measurements of electrical impedance made from a set of electrodes applied around the periphery of the body.
Journal ArticleDOI
Electrical impedance tomography
TL;DR: This article focuses on the type of EIT called adaptive current tomography (ACT) in which currents are applied simultaneously to all the electrodes, where a number of current patterns are applied, where each pattern defines the current for each electrode, and the subsequent electrode voltages are measured to generate the data required for image reconstruction.