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Using Neural Networks to Detect Anomalies in X-Ray Images Obtained with Full-Body Scanners

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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.

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Authors and Affiliations

  1. RUDN University, Moscow, 117198, Russia

    A. S. Markov, E. Yu. Kotlyarov, N. P. Anosova, V. A. Popov, Ya. M. Karandashev & D. E. Apushkinskaya

  2. Institute for Systems Analysis, Russian Academy of Sciences, Moscow, 117218, Russia

    Ya. M. Karandashev

Authors
  1. A. S. Markov
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  2. E. Yu. Kotlyarov
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  3. N. P. Anosova
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  4. V. A. Popov
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  5. Ya. M. Karandashev
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  6. D. E. Apushkinskaya
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Corresponding authors

Correspondence to A. S. Markov, E. Yu. Kotlyarov, N. P. Anosova, V. A. Popov, Ya. M. Karandashev or D. E. Apushkinskaya.

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Translated by V. Potapchouck

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Markov, A.S., Kotlyarov, E.Y., Anosova, N.P. et al. Using Neural Networks to Detect Anomalies in X-Ray Images Obtained with Full-Body Scanners. Autom Remote Control 83, 1507–1516 (2022). https://doi.org/10.1134/S00051179220100034

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  • DOI: https://doi.org/10.1134/S00051179220100034

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