Identification of Asymptomatic COVID-19 Patients on Chest CT Images Using Transformer-Based or Convolutional Neural Network–Based Deep Learning Models Article Swipe
Minyue Yin
,
Xiaolong Liang
,
Zilan Wang
,
Yijia Zhou
,
Yu He
,
Yuhan Xue
,
Jingwen Gao
,
Jiaxi Lin
,
Chenyan Yu
,
Lu Liu
,
Xiaolin Liu
,
Chao Xu
,
Jinzhou Zhu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1007/s10278-022-00754-0
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1007/s10278-022-00754-0
Novel coronavirus disease 2019 (COVID-19) has rapidly spread throughout the world; however, it is difficult for clinicians to make early diagnoses. This study is to evaluate the feasibility of using deep learning (DL) models to identify asymptomatic COVID-19 patients based on chest CT images. In this retrospective study, six DL models (Xception, NASNet, ResNet, EfficientNet, ViT, and Swin), based on convolutional neural networks (CNNs) or transformer architectures, were trained to identify asymptomatic patients with COVID-19 on chest CT images. Data from Yangzhou were randomly split into a training set ( n = 2140) and an internal-validation set ( n = 360). Data from Suzhou was the external-test set ( n = 200). Model performance was assessed by the metrics accuracy, recall, and specificity and was compared with the assessments of two radiologists. A total of 2700 chest CT images were collected in this study. In the validation dataset, the Swin model achieved the highest accuracy of 0.994, followed by the EfficientNet model (0.954). The recall and the precision of the Swin model were 0.989 and 1.000, respectively. In the test dataset, the Swin model was still the best and achieved the highest accuracy (0.980). All the DL models performed remarkably better than the two experts. Last, the time on the test set diagnosis spent by two experts—42 min, 17 s (junior); and 29 min, 43 s (senior)—was significantly higher than those of the DL models (all below 2 min). This study evaluated the feasibility of multiple DL models in distinguishing asymptomatic patients with COVID-19 from healthy subjects on chest CT images. It found that a transformer-based model, the Swin model, performed best.
Related Topics
Concepts
Convolutional neural network
Asymptomatic
Coronavirus disease 2019 (COVID-19)
Test set
Artificial intelligence
Medicine
Deep learning
Medical diagnosis
Data set
Artificial neural network
Computer science
Recall
Radiology
Pattern recognition (psychology)
Pathology
Disease
Infectious disease (medical specialty)
Philosophy
Linguistics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s10278-022-00754-0
- https://link.springer.com/content/pdf/10.1007/s10278-022-00754-0.pdf
- OA Status
- hybrid
- Cited By
- 8
- References
- 54
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4313451277
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4313451277Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/s10278-022-00754-0Digital Object Identifier
- Title
-
Identification of Asymptomatic COVID-19 Patients on Chest CT Images Using Transformer-Based or Convolutional Neural Network–Based Deep Learning ModelsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-03Full publication date if available
- Authors
-
Minyue Yin, Xiaolong Liang, Zilan Wang, Yijia Zhou, Yu He, Yuhan Xue, Jingwen Gao, Jiaxi Lin, Chenyan Yu, Lu Liu, Xiaolin Liu, Chao Xu, Jinzhou ZhuList of authors in order
- Landing page
-
https://doi.org/10.1007/s10278-022-00754-0Publisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s10278-022-00754-0.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://link.springer.com/content/pdf/10.1007/s10278-022-00754-0.pdfDirect OA link when available
- Concepts
-
Convolutional neural network, Asymptomatic, Coronavirus disease 2019 (COVID-19), Test set, Artificial intelligence, Medicine, Deep learning, Medical diagnosis, Data set, Artificial neural network, Computer science, Recall, Radiology, Pattern recognition (psychology), Pathology, Disease, Infectious disease (medical specialty), Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4, 2024: 3, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
54Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.it | 13 |
| abstract_inverted_index.of | 29, 130, 135, 156, 169, 232, 245 |
| abstract_inverted_index.on | 41, 60, 76, 209, 258 |
| abstract_inverted_index.or | 65 |
| abstract_inverted_index.to | 18, 25, 35, 70 |
| abstract_inverted_index.All | 195 |
| abstract_inverted_index.The | 164 |
| abstract_inverted_index.and | 57, 94, 122, 124, 166, 175, 189, 222 |
| abstract_inverted_index.for | 16 |
| abstract_inverted_index.has | 6 |
| abstract_inverted_index.set | 89, 97, 108, 212 |
| abstract_inverted_index.six | 49 |
| abstract_inverted_index.the | 10, 27, 106, 118, 128, 146, 149, 153, 160, 167, 170, 179, 182, 187, 191, 196, 203, 207, 210, 233, 243, 268 |
| abstract_inverted_index.two | 131, 204, 216 |
| abstract_inverted_index.was | 105, 115, 125, 185 |
| abstract_inverted_index.(DL) | 33 |
| abstract_inverted_index.(all | 236 |
| abstract_inverted_index.2019 | 4 |
| abstract_inverted_index.2700 | 136 |
| abstract_inverted_index.Data | 80, 102 |
| abstract_inverted_index.Swin | 150, 171, 183, 269 |
| abstract_inverted_index.This | 22, 240 |
| abstract_inverted_index.ViT, | 56 |
| abstract_inverted_index.best | 188 |
| abstract_inverted_index.deep | 31 |
| abstract_inverted_index.from | 81, 103, 255 |
| abstract_inverted_index.into | 86 |
| abstract_inverted_index.make | 19 |
| abstract_inverted_index.min, | 218, 224 |
| abstract_inverted_index.test | 180, 211 |
| abstract_inverted_index.than | 202, 230 |
| abstract_inverted_index.that | 264 |
| abstract_inverted_index.this | 46, 143 |
| abstract_inverted_index.time | 208 |
| abstract_inverted_index.were | 68, 83, 140, 173 |
| abstract_inverted_index.with | 74, 127, 253 |
| abstract_inverted_index.0.989 | 174 |
| abstract_inverted_index.200). | 112 |
| abstract_inverted_index.2140) | 93 |
| abstract_inverted_index.360). | 101 |
| abstract_inverted_index.Last, | 206 |
| abstract_inverted_index.Model | 113 |
| abstract_inverted_index.Novel | 1 |
| abstract_inverted_index.based | 40, 59 |
| abstract_inverted_index.below | 237 |
| abstract_inverted_index.best. | 272 |
| abstract_inverted_index.chest | 42, 77, 137, 259 |
| abstract_inverted_index.early | 20 |
| abstract_inverted_index.found | 263 |
| abstract_inverted_index.min). | 239 |
| abstract_inverted_index.model | 151, 162, 172, 184 |
| abstract_inverted_index.spent | 214 |
| abstract_inverted_index.split | 85 |
| abstract_inverted_index.still | 186 |
| abstract_inverted_index.study | 23, 241 |
| abstract_inverted_index.those | 231 |
| abstract_inverted_index.total | 134 |
| abstract_inverted_index.using | 30 |
| abstract_inverted_index.(CNNs) | 64 |
| abstract_inverted_index.0.994, | 157 |
| abstract_inverted_index.1.000, | 176 |
| abstract_inverted_index.Suzhou | 104 |
| abstract_inverted_index.Swin), | 58 |
| abstract_inverted_index.better | 201 |
| abstract_inverted_index.higher | 229 |
| abstract_inverted_index.images | 139 |
| abstract_inverted_index.model, | 267, 270 |
| abstract_inverted_index.models | 34, 51, 198, 235, 248 |
| abstract_inverted_index.neural | 62 |
| abstract_inverted_index.recall | 165 |
| abstract_inverted_index.spread | 8 |
| abstract_inverted_index.study, | 48 |
| abstract_inverted_index.study. | 144 |
| abstract_inverted_index.world; | 11 |
| abstract_inverted_index.NASNet, | 53 |
| abstract_inverted_index.ResNet, | 54 |
| abstract_inverted_index.disease | 3 |
| abstract_inverted_index.healthy | 256 |
| abstract_inverted_index.highest | 154, 192 |
| abstract_inverted_index.images. | 44, 79, 261 |
| abstract_inverted_index.metrics | 119 |
| abstract_inverted_index.rapidly | 7 |
| abstract_inverted_index.recall, | 121 |
| abstract_inverted_index.trained | 69 |
| abstract_inverted_index.(0.954). | 163 |
| abstract_inverted_index.(0.980). | 194 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.COVID-19 | 38, 75, 254 |
| abstract_inverted_index.Yangzhou | 82 |
| abstract_inverted_index.accuracy | 155, 193 |
| abstract_inverted_index.achieved | 152, 190 |
| abstract_inverted_index.assessed | 116 |
| abstract_inverted_index.compared | 126 |
| abstract_inverted_index.dataset, | 148, 181 |
| abstract_inverted_index.evaluate | 26 |
| abstract_inverted_index.experts. | 205 |
| abstract_inverted_index.followed | 158 |
| abstract_inverted_index.however, | 12 |
| abstract_inverted_index.identify | 36, 71 |
| abstract_inverted_index.learning | 32 |
| abstract_inverted_index.multiple | 246 |
| abstract_inverted_index.networks | 63 |
| abstract_inverted_index.patients | 39, 73, 252 |
| abstract_inverted_index.randomly | 84 |
| abstract_inverted_index.subjects | 257 |
| abstract_inverted_index.training | 88 |
| abstract_inverted_index.(junior); | 221 |
| abstract_inverted_index.accuracy, | 120 |
| abstract_inverted_index.collected | 141 |
| abstract_inverted_index.diagnosis | 213 |
| abstract_inverted_index.difficult | 15 |
| abstract_inverted_index.evaluated | 242 |
| abstract_inverted_index.performed | 199, 271 |
| abstract_inverted_index.precision | 168 |
| abstract_inverted_index.(COVID-19) | 5 |
| abstract_inverted_index.(Xception, | 52 |
| abstract_inverted_index.clinicians | 17 |
| abstract_inverted_index.diagnoses. | 21 |
| abstract_inverted_index.remarkably | 200 |
| abstract_inverted_index.throughout | 9 |
| abstract_inverted_index.validation | 147 |
| abstract_inverted_index.assessments | 129 |
| abstract_inverted_index.coronavirus | 2 |
| abstract_inverted_index.feasibility | 28, 244 |
| abstract_inverted_index.performance | 114 |
| abstract_inverted_index.specificity | 123 |
| abstract_inverted_index.transformer | 66 |
| abstract_inverted_index.EfficientNet | 161 |
| abstract_inverted_index.asymptomatic | 37, 72, 251 |
| abstract_inverted_index.experts—42 | 217 |
| abstract_inverted_index.EfficientNet, | 55 |
| abstract_inverted_index.convolutional | 61 |
| abstract_inverted_index.external-test | 107 |
| abstract_inverted_index.radiologists. | 132 |
| abstract_inverted_index.respectively. | 177 |
| abstract_inverted_index.retrospective | 47 |
| abstract_inverted_index.significantly | 228 |
| abstract_inverted_index.(senior)—was | 227 |
| abstract_inverted_index.architectures, | 67 |
| abstract_inverted_index.distinguishing | 250 |
| abstract_inverted_index.transformer-based | 266 |
| abstract_inverted_index.internal-validation | 96 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 13 |
| citation_normalized_percentile.value | 0.86580057 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |