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Ultrasound tomography enhancement by signal feature extraction with modular machine learning method

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  • Bartłomiej Baran
  • Dariusz Majerek
  • Piotr Szyszka
  • Dariusz Wójcik
  • Tomasz Rymarczyk

Abstract

Robust and reliable diagnostic methods are desired in various types of industries. This article presents a novel approach to object detection in industrial or general ultrasound tomography. The key idea is to analyze the time-dependent ultrasonic signal recorded by three independent transducers of an experimental system. It focuses on finding common or related characteristics of these signals using custom-designed deep neural network models. In principle, models use convolution layers to extract common features of signals, which are passed to dense layers responsible for predicting the number of objects or their locations and sizes. Predicting the number and properties of objects are characterized by a high value of the coefficient of determination R2 = 99.8% and R2 = 98.4%, respectively. The proposed solution can result in a reliable and low-cost method of object detection for various industry sectors.

Suggested Citation

  • Bartłomiej Baran & Dariusz Majerek & Piotr Szyszka & Dariusz Wójcik & Tomasz Rymarczyk, 2024. "Ultrasound tomography enhancement by signal feature extraction with modular machine learning method," PLOS ONE, Public Library of Science, vol. 19(1), pages 1-13, January.
    • Handle: RePEc:plo:pone00:0297496
    DOI: 10.1371/journal.pone.0297496
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    References listed on IDEAS

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    1. Dariusz Majerek & Tomasz Rymarczyk & Dariusz Wójcik & Edward Kozłowski & Magda Rzemieniak & Janusz Gudowski & Konrad Gauda, 2021. "Machine Learning and Deterministic Approach to the Reflective Ultrasound Tomography," Energies, MDPI, vol. 14(22), pages 1-19, November.
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