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COVID-RDNet: A novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images
Corona virus disease 2019 (COVID-19) testing relies on traditional screening methods, which require a lot of manpower and material resources. Recently, to effectively reduce the damage caused by radiation and enhance effectiveness, deep learning of classifying COVID-19 negative and positive using th...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9353669/ https://www.ncbi.nlm.nih.gov/pubmed/35945982 http://dx.doi.org/10.1016/j.bbe.2022.07.009 |
_version_ | 1784762910501765120 |
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author | Fang, Lingling Wang, Xin |
author_facet | Fang, Lingling Wang, Xin |
author_sort | Fang, Lingling |
collection | PubMed |
description | Corona virus disease 2019 (COVID-19) testing relies on traditional screening methods, which require a lot of manpower and material resources. Recently, to effectively reduce the damage caused by radiation and enhance effectiveness, deep learning of classifying COVID-19 negative and positive using the mixed dataset by CT and X-rays images have achieved remarkable research results. However, the details presented on CT and X-ray images have pathological diversity and similarity features, thus increasing the difficulty for physicians to judge specific cases. On this basis, this paper proposes a novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images. To solve the problem of feature similarity between lung diseases and COVID-19, the extracted features are enhanced by an adaptive region enhancement algorithm. Besides, the depth network based on the residual blocks and the dense blocks is trained and tested. On the one hand, the residual blocks effectively improve the accuracy of the model and the non-linear COVID-19 features are obtained by cross-layer link. On the other hand, the dense blocks effectively improve the robustness of the model by connecting local and abstract information. On mixed X-ray and CT datasets, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under curve (AUC), and accuracy can all reach 0.99. On the basis of respecting patient privacy and ethics, the proposed algorithm using the mixed dataset from real cases can effectively assist doctors in performing the accurate COVID-19 negative and positive classification to determine the infection status of patients. |
format | Online Article Text |
id | pubmed-9353669 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93536692022-08-05 COVID-RDNet: A novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images Fang, Lingling Wang, Xin Biocybern Biomed Eng Original Research Article Corona virus disease 2019 (COVID-19) testing relies on traditional screening methods, which require a lot of manpower and material resources. Recently, to effectively reduce the damage caused by radiation and enhance effectiveness, deep learning of classifying COVID-19 negative and positive using the mixed dataset by CT and X-rays images have achieved remarkable research results. However, the details presented on CT and X-ray images have pathological diversity and similarity features, thus increasing the difficulty for physicians to judge specific cases. On this basis, this paper proposes a novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images. To solve the problem of feature similarity between lung diseases and COVID-19, the extracted features are enhanced by an adaptive region enhancement algorithm. Besides, the depth network based on the residual blocks and the dense blocks is trained and tested. On the one hand, the residual blocks effectively improve the accuracy of the model and the non-linear COVID-19 features are obtained by cross-layer link. On the other hand, the dense blocks effectively improve the robustness of the model by connecting local and abstract information. On mixed X-ray and CT datasets, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under curve (AUC), and accuracy can all reach 0.99. On the basis of respecting patient privacy and ethics, the proposed algorithm using the mixed dataset from real cases can effectively assist doctors in performing the accurate COVID-19 negative and positive classification to determine the infection status of patients. Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. 2022 2022-08-05 /pmc/articles/PMC9353669/ /pubmed/35945982 http://dx.doi.org/10.1016/j.bbe.2022.07.009 Text en © 2022 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Original Research Article Fang, Lingling Wang, Xin COVID-RDNet: A novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images |
title | COVID-RDNet: A novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images |
title_full | COVID-RDNet: A novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images |
title_fullStr | COVID-RDNet: A novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images |
title_full_unstemmed | COVID-RDNet: A novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images |
title_short | COVID-RDNet: A novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images |
title_sort | covid-rdnet: a novel coronavirus pneumonia classification model using the mixed dataset by ct and x-rays images |
topic | Original Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9353669/ https://www.ncbi.nlm.nih.gov/pubmed/35945982 http://dx.doi.org/10.1016/j.bbe.2022.07.009 |
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