Classification of stroke using neural networks in electrical impedance tomography

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Agnelli , J P , Çöl , A , Lassas , M , Murthy , R , Santacesaria , M & Siltanen , S 2020 , ' Classification of stroke using neural networks in electrical impedance tomography ' , Inverse Problems , vol. 36 , no. 11 , 115008 . https://doi.org/10.1088/1361-6420/abbdcd

Title: Classification of stroke using neural networks in electrical impedance tomography
Author: Agnelli, J. P.; Çöl, A.; Lassas, M.; Murthy, R.; Santacesaria, M.; Siltanen, S.
Contributor: University of Helsinki, Universidad Nacional de Córdoba (UNC)
University of Helsinki, Department of Mathematics and Statistics
University of Helsinki, Department of Mathematics and Statistics
University of Helsinki, University of Genoa
University of Helsinki, Inverse Problems
Date: 2020-11
Language: eng
Number of pages: 26
Belongs to series: Inverse Problems
ISSN: 0266-5611
URI: http://hdl.handle.net/10138/335615
Abstract: Electrical impedance tomography (EIT) is an emerging non-invasive medical imaging modality. It is based on feeding electrical currents into the patient, measuring the resulting voltages at the skin, and recovering the internal conductivity distribution. The mathematical task of EIT image reconstruction is a nonlinear and ill-posed inverse problem. Therefore any EIT image reconstruction method needs to be regularized, typically resulting in blurred images. One promising application is stroke-EIT, or classification of stroke into either ischemic or hemorrhagic. Ischemic stroke involves a blood clot, preventing blood flow to a part of the brain causing a low-conductivity region. Hemorrhagic stroke means bleeding in the brain causing a high-conductivity region. In both cases the symptoms are identical, so a cost-effective and portable classification device is needed. Typical EIT images are not optimal for stroke-EIT because of blurriness. This paper explores the possibilities of machine learning in improving the classification results. Two paradigms are compared: (a) learning from the EIT data, that is Dirichlet-to-Neumann maps and (b) extracting robust features from data and learning from them. The features of choice are virtual hybrid edge detection (VHED) functions (Greenleaf et al 2018 Anal. PDE 11) that have a geometric interpretation and whose computation from EIT data does not involve calculating a full image of the conductivity. We report the measures of accuracy, sensitivity and specificity of the networks trained with EIT data and VHED functions separately. Computational evidence based on simulated noisy EIT data suggests that the regularized grey-box paradigm (b) leads to significantly better classification results than the black-box paradigm (a).
Subject: inverse problems
EIT
neural networks
VHED function
classification
D-BAR METHOD
INVERSE PROBLEMS
UNIQUENESS
BRAIN
111 Mathematics
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