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Avtomatika i Telemekhanika, 2022, Issue 10, Pages 23–34
DOI: https://doi.org/10.31857/S0005231022100038 (Mi at16048) 		 
This article is cited in 2 scientific papers (total in 2 papers)

Topical issue

Using neural networks to detect anomalies in X-ray images obtained with full-body scanners

A. S. Markova, E. Yu. Kotlyarova, N. P. Anosovaa, V. A. Popova, I. Karandashevba, D. E. Apushkinskayaa

a RUDN University, Moscow, 117198 Russia
b Institute for Systems Analysis, Russian Academy of Sciences, Moscow, 117218 Russia
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DOI: https://doi.org/10.31857/S0005231022100038
Abstract: In this paper, we solve the problem of detecting anomalies in X-ray images obtained by full-body scanners (FBSs). The paper describes the sequence and description of image preprocessing methods used to convert the original images obtained with an FBS to images with visually distinguishable anomalies. Examples of processed images are given. The first (preliminary) results of using a neural network for anomaly detection are shown.
Keywords: full-body scanner, X-ray image, anomaly detection, image histogram equalization, neural network, U-2-Net.
Presented by the member of Editorial Board: A. A. Lazarev

Received: 01.02.2022
Revised: 31.05.2022
Accepted: 29.06.2022
English version:
Automation and Remote Control, 2022, Volume 83, Issue 10, Pages 1507–1516
DOI: https://doi.org/10.1134/S00051179220100034
Bibliographic databases:
Document Type: Article
Language: Russian
Citation: A. S. Markov, E. Yu. Kotlyarov, N. P. Anosova, V. A. Popov, I. Karandashev, D. E. Apushkinskaya, “Using neural networks to detect anomalies in X-ray images obtained with full-body scanners”, Avtomat. i Telemekh., 2022, no. 10, 23–34; Autom. Remote Control, 83:10 (2022), 1507–1516
Citation in format AMSBIB
\Bibitem{MarKotAno22}
\by A.~S.~Markov, E.~Yu.~Kotlyarov, N.~P.~Anosova, V.~A.~Popov, I.~Karandashev, D.~E.~Apushkinskaya
\paper Using neural networks to detect anomalies in X-ray images obtained with full-body scanners
\jour Avtomat. i Telemekh.
\yr 2022
\issue 10
\pages 23--34
\mathnet{http://mi.mathnet.ru/at16048}
\crossref{https://doi.org/10.31857/S0005231022100038}
\edn{https://elibrary.ru/AJXBSP}
\transl
\jour Autom. Remote Control
\yr 2022
\vol 83
\issue 10
\pages 1507--1516
\crossref{https://doi.org/10.1134/S00051179220100034}
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  • https://www.mathnet.ru/eng/at/y2022/i10/p23
    • This publication is cited in the following 2 articles:
    Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
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    References:16
    First page:18
     
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