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Convolutional Neural Networks for Challenges in Automated Nuclide Identification
Improvements in Radio-Isotope IDentification (RIID) algorithms have seen a resurgence in interest with the increased accessibility of machine learning models. Convolutional Neural Network (CNN)-based models have been developed to identify arbitrary mixtures of unstable nuclides from gamma spectra. I...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8347253/ https://www.ncbi.nlm.nih.gov/pubmed/34372475 http://dx.doi.org/10.3390/s21155238 |
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author | Turner, Anthony N. Wheldon, Carl Wheldon, Tzany Kokalova Gilbert, Mark R. Packer, Lee W. Burns, Jonathan Freer, Martin |
author_facet | Turner, Anthony N. Wheldon, Carl Wheldon, Tzany Kokalova Gilbert, Mark R. Packer, Lee W. Burns, Jonathan Freer, Martin |
author_sort | Turner, Anthony N. |
collection | PubMed |
description | Improvements in Radio-Isotope IDentification (RIID) algorithms have seen a resurgence in interest with the increased accessibility of machine learning models. Convolutional Neural Network (CNN)-based models have been developed to identify arbitrary mixtures of unstable nuclides from gamma spectra. In service of this, methods for the simulation and pre-processing of training data were also developed. The implementation of 1D multi-class, multi-label CNNs demonstrated good generalisation to real spectra with poor statistics and significant gain shifts. It is also shown that even basic CNN architectures prove reliable for RIID under the challenging conditions of heavy shielding and close source geometries, and may be extended to generalised solutions for pragmatic RIID. |
format | Online Article Text |
id | pubmed-8347253 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83472532021-08-08 Convolutional Neural Networks for Challenges in Automated Nuclide Identification Turner, Anthony N. Wheldon, Carl Wheldon, Tzany Kokalova Gilbert, Mark R. Packer, Lee W. Burns, Jonathan Freer, Martin Sensors (Basel) Article Improvements in Radio-Isotope IDentification (RIID) algorithms have seen a resurgence in interest with the increased accessibility of machine learning models. Convolutional Neural Network (CNN)-based models have been developed to identify arbitrary mixtures of unstable nuclides from gamma spectra. In service of this, methods for the simulation and pre-processing of training data were also developed. The implementation of 1D multi-class, multi-label CNNs demonstrated good generalisation to real spectra with poor statistics and significant gain shifts. It is also shown that even basic CNN architectures prove reliable for RIID under the challenging conditions of heavy shielding and close source geometries, and may be extended to generalised solutions for pragmatic RIID. MDPI 2021-08-03 /pmc/articles/PMC8347253/ /pubmed/34372475 http://dx.doi.org/10.3390/s21155238 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Turner, Anthony N. Wheldon, Carl Wheldon, Tzany Kokalova Gilbert, Mark R. Packer, Lee W. Burns, Jonathan Freer, Martin Convolutional Neural Networks for Challenges in Automated Nuclide Identification |
title | Convolutional Neural Networks for Challenges in Automated Nuclide Identification |
title_full | Convolutional Neural Networks for Challenges in Automated Nuclide Identification |
title_fullStr | Convolutional Neural Networks for Challenges in Automated Nuclide Identification |
title_full_unstemmed | Convolutional Neural Networks for Challenges in Automated Nuclide Identification |
title_short | Convolutional Neural Networks for Challenges in Automated Nuclide Identification |
title_sort | convolutional neural networks for challenges in automated nuclide identification |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8347253/ https://www.ncbi.nlm.nih.gov/pubmed/34372475 http://dx.doi.org/10.3390/s21155238 |
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