Neural networks for classification of strokes in electrical impedance tomography on a 3D head model

<abstract><p>We consider the problem of the detection of brain hemorrhages from three-dimensional (3D) electrical impedance tomography (EIT) measurements. This is a condition requiring urgent treatment for which EIT might provide a portable and quick diagnosis. We employ two neural network architectures - a fully connected and a convolutional one - for the classification of hemorrhagic and ischemic strokes. The networks are trained on a dataset with $ 40\, 000 $ samples of synthetic electrode measurements generated with the complete electrode model on realistic heads with a 3-layer structure. We consider changes in head anatomy and layers, electrode position, measurement noise and conductivity values. We then test the networks on several datasets of unseen EIT data, with more complex stroke modeling (different shapes and volumes), higher levels of noise and different amounts of electrode misplacement. On most test datasets we achieve $ \geq 90\% $ average accuracy with fully connected neural networks, while the convolutional ones display an average accuracy $ \geq 80\% $. Despite the use of simple neural network architectures, the results obtained are very promising and motivate the applications of EIT-based classification methods on real phantoms and ultimately on human patients.</p></abstract>

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IMPROVING GENERALIZATION OF NEURAL NETWORKS THROUGH PRUNING

International Journal of Neural Systems ◽

10.1142/s0129065791000352 ◽

1991 ◽

Vol 01 (04) ◽

pp. 317-326 ◽

Cited By ~ 49

Author(s):

Hans Henrik Thodberg

Keyword(s):

Neural Network ◽

Neural Networks ◽

Internal Representation ◽

Network Architectures ◽

Ockham’S Razor ◽

Ockham's Razor ◽

Neural Network Architectures

A technique for constructing neural network architectures with better ability to generalize is presented under the name Ockham's Razor: several networks are trained and then pruned by removing connections one by one and retraining. The networks which achieve fewest connections generalize best. The method is tested on a classification of bit strings (the contiguity problem): the optimal architecture emerges, resulting in perfect generalization. The internal representation of the network changes substantially during the retraining, and this distinguishes the method from previous pruning studies.

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Use of artificial intelligence and neural networks for analysis and gesture detection in electrical impedance tomography

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2020-3126 ◽

2020 ◽

Vol 6 (3) ◽

pp. 489-492

Author(s):

Christian Gibas ◽

Luca Mülln ◽

Rainer Brück

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Neural Networks ◽

Electrical Impedance Tomography ◽

Electrical Impedance ◽

Impedance Tomography ◽

Precise Analysis ◽

User Friendly ◽

And Control

AbstractArtificial intelligence and neural networks are getting more and more relevant for several types of application. The field of prosthesis technology currently uses electromyography for controllable prosthesis. The precision of the control suffers from the use of EMG. More precise and more collected data with the help of EIT allows a much more precise analysis and control of the prosthesis. In this paper a neural network for gesture detection using EIT is developed and presented in a user-friendly way.

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Classification of Fermi-LAT sources with deep learning using energy and time spectra

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/stab2389 ◽

2021 ◽

Vol 507 (3) ◽

pp. 4061-4073

Author(s):

Thorben Finke ◽

Michael Krämer ◽

Silvia Manconi

Keyword(s):

Neural Network ◽

Neural Networks ◽

Active Galactic Nuclei ◽

Photon Energy ◽

Deep Neural Network ◽

Gamma Ray ◽

Galactic Nuclei ◽

Network Architectures ◽

Neural Network Architectures

ABSTRACT Despite the growing number of gamma-ray sources detected by the Fermi-Large Area Telescope (LAT), about one-third of the sources in each survey remains of uncertain type. We present a new deep neural network approach for the classification of unidentified or unassociated gamma-ray sources in the last release of the Fermi-LAT catalogue (4FGL-DR2) obtained with 10 yr of data. In contrast to previous work, our method directly uses the measurements of the photon energy spectrum and time series as input for the classification, instead of specific, human-crafted features. Dense neural networks, and for the first time in the context of gamma-ray source classification recurrent neural networks, are studied in depth. We focus on the separation between extragalactic sources, i.e. active galactic nuclei, and Galactic pulsars, and on the further classification of pulsars into young and millisecond pulsars. Our neural network architectures provide powerful classifiers, with a performance that is comparable to previous analyses based on human-crafted features. Our benchmark neural network predicts that of the sources of uncertain type in the 4FGL-DR2 catalogue, 1050 are active galactic nuclei and 78 are Galactic pulsars, with both classes following the expected sky distribution and the clustering in the variability–curvature plane. We investigate the problem of sample selection bias by testing our architectures against a cross-match test data set using an older catalogue, and propose a feature selection algorithm using autoencoders. Our list of high-confidence candidate sources labelled by the neural networks provides a set of targets for further multiwavelength observations addressed to identify their nature. The deep neural network architectures we develop can be easily extended to include specific features, as well as multiwavelength data on the source photon energy and time spectra coming from different instruments.

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Classification of stroke using neural networks in electrical impedance tomography

Inverse Problems ◽

10.1088/1361-6420/abbdcd ◽

2020 ◽

Vol 36 (11) ◽

pp. 115008

Author(s):

J P Agnelli ◽

A Çöl ◽

M Lassas ◽

R Murthy ◽

M Santacesaria ◽

...

Keyword(s):

Neural Networks ◽

Electrical Impedance Tomography ◽

Electrical Impedance ◽

Impedance Tomography

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Using Summary Layers to Probe Neural Network Behaviour

South African Computer Journal ◽

10.18489/sacj.v32i2.861 ◽

2020 ◽

Vol 32 (2) ◽

Author(s):

Marelie Hattingh Davel

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Neural Networks ◽

Network Architectures ◽

Feedforward Networks ◽

Empirical Results ◽

Analysis Process ◽

Generalisation Ability ◽

Neural Network Architectures ◽

Fully Connected

No framework exists that can explain and predict the generalisation ability of deep neural networks in general circumstances. In fact, this question has not been answered for some of the least complicated of neural network architectures: fully-connected feedforward networks with rectified linear activations and a limited number of hidden layers. For such an architecture, we show how adding a summary layer to the network makes it more amenable to analysis, and allows us to define the conditions that are required to guarantee that a set of samples will all be classified correctly. This process does not describe the generalisation behaviour of these networks, but produces a number of metrics that are useful for probing their learning and generalisation behaviour. We support the analytical conclusions with empirical results, both to confirm that the mathematical guarantees hold in practice, and to demonstrate the use of the analysis process.

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