Anomaly Location Detection with Electrical Impedance Tomography Using Multilayer Perceptrons

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Abstract

Electrical impedance tomography (EIT) does imaging by solving a nonlinear ill-posed inverse problem. Recently, there has been an increasing interest in solving this problem with artificial neural networks. However, a systematic understanding of the optimal neural network architecture for this problem is still lacking. This paper compares the performance of different multilayer perceptron algorithms for detecting the location of an anomaly on a sensing surface by solving the EIT inverse problem. We generate synthetic data with varying anomaly sizes/locations and compare a wide range of multilayer perceptron algorithms by simulations. Our results indicate that increasing the dimensions of the perceptron improves performance, but this improvement saturates soon. The best performance is achieved when using the multilayer perceptron for regression and Gaussian noise addition as the regularization method.
Original languageEnglish
Title of host publicationProceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020
PublisherIEEE
Pages1-6
Number of pages6
ISBN (Electronic)9781728166629
DOIs
Publication statusPublished - 23 Sep 2020
MoE publication typeA4 Article in a conference publication
EventIEEE International Workshop on Machine Learning for Signal Processing - Espoo, Finland
Duration: 21 Sep 202024 Sep 2020
Conference number: 30
https://ieeemlsp.cc

Publication series

NameMachine Learning for Signal Processing
PublisherIEEE
ISSN (Electronic)1551-2541

Workshop

WorkshopIEEE International Workshop on Machine Learning for Signal Processing
Abbreviated titleMLSP
CountryFinland
CityEspoo
Period21/09/202024/09/2020
Internet address

Keywords

  • electrical impedance tomography (EIT)
  • multilayer perceptrons
  • anomaly detection

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