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Automated laryngeal mass detection algorithm for home-based self-screening test based on convolutional neural network
BACKGROUND: Early detection of laryngeal masses without periodic visits to hospitals is essential for improving the possibility of full recovery and the long-term survival ratio after prompt treatment, as well as reducing the risk of clinical infection. RESULTS: We first propose a convolutional neur...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8144695/ https://www.ncbi.nlm.nih.gov/pubmed/34034766 http://dx.doi.org/10.1186/s12938-021-00886-4 |
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author | Kim, Gun Ho Sung, Eui-Suk Nam, Kyoung Won |
author_facet | Kim, Gun Ho Sung, Eui-Suk Nam, Kyoung Won |
author_sort | Kim, Gun Ho |
collection | PubMed |
description | BACKGROUND: Early detection of laryngeal masses without periodic visits to hospitals is essential for improving the possibility of full recovery and the long-term survival ratio after prompt treatment, as well as reducing the risk of clinical infection. RESULTS: We first propose a convolutional neural network model for automated laryngeal mass detection based on diagnostic images captured at hospitals. Thereafter, we propose a pilot system, composed of an embedded controller, a camera module, and an LCD display, that can be utilized for a home-based self-screening test. In terms of evaluating the model’s performance, the experimental results indicated a final validation loss of 0.9152 and a F1-score of 0.8371 before post-processing. Additionally, the F1-score of the original computer algorithm with respect to 100 randomly selected color-printed test images was 0.8534 after post-processing while that of the embedded pilot system was 0.7672. CONCLUSIONS: The proposed technique is expected to increase the ratio of early detection of laryngeal masses without the risk of clinical infection spread, which could help improve convenience and ensure safety of individuals, patients, and medical staff. |
format | Online Article Text |
id | pubmed-8144695 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-81446952021-05-25 Automated laryngeal mass detection algorithm for home-based self-screening test based on convolutional neural network Kim, Gun Ho Sung, Eui-Suk Nam, Kyoung Won Biomed Eng Online Research BACKGROUND: Early detection of laryngeal masses without periodic visits to hospitals is essential for improving the possibility of full recovery and the long-term survival ratio after prompt treatment, as well as reducing the risk of clinical infection. RESULTS: We first propose a convolutional neural network model for automated laryngeal mass detection based on diagnostic images captured at hospitals. Thereafter, we propose a pilot system, composed of an embedded controller, a camera module, and an LCD display, that can be utilized for a home-based self-screening test. In terms of evaluating the model’s performance, the experimental results indicated a final validation loss of 0.9152 and a F1-score of 0.8371 before post-processing. Additionally, the F1-score of the original computer algorithm with respect to 100 randomly selected color-printed test images was 0.8534 after post-processing while that of the embedded pilot system was 0.7672. CONCLUSIONS: The proposed technique is expected to increase the ratio of early detection of laryngeal masses without the risk of clinical infection spread, which could help improve convenience and ensure safety of individuals, patients, and medical staff. BioMed Central 2021-05-25 /pmc/articles/PMC8144695/ /pubmed/34034766 http://dx.doi.org/10.1186/s12938-021-00886-4 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Kim, Gun Ho Sung, Eui-Suk Nam, Kyoung Won Automated laryngeal mass detection algorithm for home-based self-screening test based on convolutional neural network |
title | Automated laryngeal mass detection algorithm for home-based self-screening test based on convolutional neural network |
title_full | Automated laryngeal mass detection algorithm for home-based self-screening test based on convolutional neural network |
title_fullStr | Automated laryngeal mass detection algorithm for home-based self-screening test based on convolutional neural network |
title_full_unstemmed | Automated laryngeal mass detection algorithm for home-based self-screening test based on convolutional neural network |
title_short | Automated laryngeal mass detection algorithm for home-based self-screening test based on convolutional neural network |
title_sort | automated laryngeal mass detection algorithm for home-based self-screening test based on convolutional neural network |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8144695/ https://www.ncbi.nlm.nih.gov/pubmed/34034766 http://dx.doi.org/10.1186/s12938-021-00886-4 |
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