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231225s2021 xx |||||o 00| ||eng c |
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|a 10.1002/ima.22595
|2 doi
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|a pubmed24n1516.xml
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|a eng
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| 100 |
1 |
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|a Cho, Yongwon
|e verfasserin
|4 aut
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| 245 |
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|a Deep convolution neural networks to differentiate between COVID-19 and other pulmonary abnormalities on chest radiographs
|b Evaluation using internal and external datasets
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|c 2021
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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|a Date Revised 29.08.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © 2021 Wiley Periodicals LLC.
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|a We aimed to evaluate the performance of convolutional neural networks (CNNs) in the classification of coronavirus disease 2019 (COVID-19) disease using normal, pneumonia, and COVID-19 chest radiographs (CXRs). First, we collected 9194 CXRs from open datasets and 58 from the Korea University Anam Hospital (KUAH). The number of normal, pneumonia, and COVID-19 CXRs were 4580, 3884, and 730, respectively. The CXRs obtained from the open dataset were randomly assigned to the training, tuning, and test sets in a 70:10:20 ratio. For external validation, the KUAH (20 normal, 20 pneumonia, and 18 COVID-19) dataset, verified by radiologists using computed tomography, was used. Subsequently, transfer learning was conducted using DenseNet169, InceptionResNetV2, and Xception to identify COVID-19 using open datasets (internal) and the KUAH dataset (external) with histogram matching. Gradient-weighted class activation mapping was used for the visualization of abnormal patterns in CXRs. The average AUC and accuracy of the multiscale and mixed-COVID-19Net using three CNNs over five folds were (0.99 ± 0.01 and 92.94% ± 0.45%), (0.99 ± 0.01 and 93.12% ± 0.23%), and (0.99 ± 0.01 and 93.57% ± 0.29%), respectively, using the open datasets (internal). Furthermore, these values were (0.75 and 74.14%), (0.72 and 68.97%), and (0.77 and 68.97%), respectively, for the best model among the fivefold cross-validation with the KUAH dataset (external) using domain adaptation. The various state-of-the-art models trained on open datasets show satisfactory performance for clinical interpretation. Furthermore, the domain adaptation for external datasets was found to be important for detecting COVID-19 as well as other diseases
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|a Journal Article
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|a COVID‐19
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| 650 |
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|a chest radiography
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| 650 |
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|a computer‐aided diagnosis (CAD)
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| 650 |
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|a deep learning
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| 650 |
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|a lung diseases
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| 700 |
1 |
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|a Hwang, Sung Ho
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Oh, Yu-Whan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ham, Byung-Joo
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Kim, Min Ju
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Park, Beom Jin
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t International journal of imaging systems and technology
|d 1990
|g 31(2021), 3 vom: 28. Sept., Seite 1087-1104
|w (DE-627)NLM098193090
|x 0899-9457
|7 nnns
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| 773 |
1 |
8 |
|g volume:31
|g year:2021
|g number:3
|g day:28
|g month:09
|g pages:1087-1104
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| 856 |
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|u http://dx.doi.org/10.1002/ima.22595
|3 Volltext
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