COVID-CXNet: Detecting COVID-19 in frontal chest X-ray images using deep learning
One of the primary clinical observations for screening the novel coronavirus is capturing a chest x-ray image. In most patients, a chest x-ray contains abnormalities, such as consolidation, resulting from COVID-19 viral pneumonia. In this study, research is conducted on efficiently detecting imaging...
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| Published in | Multimedia tools and applications Vol. 81; no. 21; pp. 30615 - 30645 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
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Springer US
01.09.2022
Springer Nature B.V |
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| Abstract | One of the primary clinical observations for screening the novel coronavirus is capturing a chest x-ray image. In most patients, a chest x-ray contains abnormalities, such as consolidation, resulting from COVID-19 viral pneumonia. In this study, research is conducted on efficiently detecting imaging features of this type of pneumonia using deep convolutional neural networks in a large dataset. It is demonstrated that simple models, alongside the majority of pretrained networks in the literature, focus on irrelevant features for decision-making. In this paper, numerous chest x-ray images from several sources are collected, and one of the largest publicly accessible datasets is prepared. Finally, using the transfer learning paradigm, the well-known CheXNet model is utilized to develop COVID-CXNet. This powerful model is capable of detecting the novel coronavirus pneumonia based on relevant and meaningful features with precise localization. COVID-CXNet is a step towards a fully automated and robust COVID-19 detection system. |
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| AbstractList | One of the primary clinical observations for screening the novel coronavirus is capturing a chest x-ray image. In most patients, a chest x-ray contains abnormalities, such as consolidation, resulting from COVID-19 viral pneumonia. In this study, research is conducted on efficiently detecting imaging features of this type of pneumonia using deep convolutional neural networks in a large dataset. It is demonstrated that simple models, alongside the majority of pretrained networks in the literature, focus on irrelevant features for decision-making. In this paper, numerous chest x-ray images from several sources are collected, and one of the largest publicly accessible datasets is prepared. Finally, using the transfer learning paradigm, the well-known CheXNet model is utilized to develop COVID-CXNet. This powerful model is capable of detecting the novel coronavirus pneumonia based on relevant and meaningful features with precise localization. COVID-CXNet is a step towards a fully automated and robust COVID-19 detection system. One of the primary clinical observations for screening the novel coronavirus is capturing a chest x-ray image. In most patients, a chest x-ray contains abnormalities, such as consolidation, resulting from COVID-19 viral pneumonia. In this study, research is conducted on efficiently detecting imaging features of this type of pneumonia using deep convolutional neural networks in a large dataset. It is demonstrated that simple models, alongside the majority of pretrained networks in the literature, focus on irrelevant features for decision-making. In this paper, numerous chest x-ray images from several sources are collected, and one of the largest publicly accessible datasets is prepared. Finally, using the transfer learning paradigm, the well-known CheXNet model is utilized to develop COVID-CXNet. This powerful model is capable of detecting the novel coronavirus pneumonia based on relevant and meaningful features with precise localization. COVID-CXNet is a step towards a fully automated and robust COVID-19 detection system.One of the primary clinical observations for screening the novel coronavirus is capturing a chest x-ray image. In most patients, a chest x-ray contains abnormalities, such as consolidation, resulting from COVID-19 viral pneumonia. In this study, research is conducted on efficiently detecting imaging features of this type of pneumonia using deep convolutional neural networks in a large dataset. It is demonstrated that simple models, alongside the majority of pretrained networks in the literature, focus on irrelevant features for decision-making. In this paper, numerous chest x-ray images from several sources are collected, and one of the largest publicly accessible datasets is prepared. Finally, using the transfer learning paradigm, the well-known CheXNet model is utilized to develop COVID-CXNet. This powerful model is capable of detecting the novel coronavirus pneumonia based on relevant and meaningful features with precise localization. COVID-CXNet is a step towards a fully automated and robust COVID-19 detection system. |
| Author | Haghanifar, Arman Deivalakshmi, S. Ko, Seokbum Majdabadi, Mahdiyar Molahasani Choi, Younhee |
| Author_xml | – sequence: 1 givenname: Arman surname: Haghanifar fullname: Haghanifar, Arman organization: Division of Biomedical Engineering, University of Saskatchewan – sequence: 2 givenname: Mahdiyar Molahasani surname: Majdabadi fullname: Majdabadi, Mahdiyar Molahasani organization: Department of Electrical & Computer EngineeringUniversity of Saskatchewan – sequence: 3 givenname: Younhee surname: Choi fullname: Choi, Younhee organization: Department of Electrical & Computer EngineeringUniversity of Saskatchewan – sequence: 4 givenname: S. surname: Deivalakshmi fullname: Deivalakshmi, S. organization: National Institute of Technology – sequence: 5 givenname: Seokbum orcidid: 0000-0002-9287-317X surname: Ko fullname: Ko, Seokbum email: seokbum.ko@usask.ca organization: Department of Electrical & Computer EngineeringUniversity of Saskatchewan |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35431611$$D View this record in MEDLINE/PubMed |
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| Keywords | COVID-19 Imaging features CheXNet Convolutional neural networks Chest X-ray |
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| SubjectTerms | Abnormalities Artificial neural networks Computer Communication Networks Computer Science Coronaviruses COVID-19 Data Structures and Information Theory Datasets Decision making Deep learning Multimedia Information Systems Neural networks Pneumonia Special Purpose and Application-Based Systems Viral diseases X-rays |
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| Title | COVID-CXNet: Detecting COVID-19 in frontal chest X-ray images using deep learning |
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