A cascaded convolutional neural networks for stroke detection imaging

In recent years, electrical impedance tomography has widely been used in stroke detection. To improve the prediction accuracy and anti-noise ability of the system, the inverse problem of electrical impedance tomography needs to be solved, for which cascade convolutional neural networks are used. The...

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Bibliographic Details
Published in:Review of scientific instruments 2023-11, Vol.94 (11)
Main Authors: Liu, Jinzhen, He, Xiaochuan, Xiong, Hui
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Electrical impedance
Image resolution
Inverse problems
Noise prediction
Scientific apparatus & instruments
Tomography
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Summary:In recent years, electrical impedance tomography has widely been used in stroke detection. To improve the prediction accuracy and anti-noise ability of the system, the inverse problem of electrical impedance tomography needs to be solved, for which cascade convolutional neural networks are used. The proposed network is divided into two parts so that the advantages can be compounded when parts of a network are cascaded together. To get high-resolution imaging, an optimized network based on encoding and decoding is designed in the first part. The second part is composed of a residual module, which is used to extract the characteristics of voltage information and ensure that no information is lost. The anti-noise performance of the network is better than other networks. In physical experiments, it is also proved that the algorithm can roughly restore the location of the object in the field.
ISSN:0034-6748
1089-7623
DOI:10.1063/5.0167592