ArticleInterdisciplinary sciences, computational life sciences2021
Accurately Discriminating COVID-19 from Viral and Bacterial Pneumonia According to CT Images Via Deep Learning.
Article in Interdisciplinary sciences, computational life sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 29 citations in OpenAlex.
- Distinguishing Between Bacterial and Viral Community-Acquired Pneumonia in Hospitalized Adults: The Importance of Clinical, Laboratory, and Imaging Findings.Pulmonary medicine · 2026Article
- Artificial intelligence for segmentation and classification of lobar, lobular, and interstitial pneumonia using case-specific CT information.Quantitative imaging in medicine and surgery · 2024Article
- The Medical Complexity of Newly Admitted Long-Term Care Residents Before and During the COVID-19 Pandemic in Ontario, British Columbia, and Alberta: A Serial Cross-Sectional Study.Health services insights · 2024Article
- Review
- Morphological appearance of the B.1.1.7 mutation of the novel coronavirus 2 (SARS-CoV-2) in chest CT.Quantitative imaging in medicine and surgery · 2023Article
- Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review.SN computer science · 2023Article
- Clinical and Laboratory Approach to Diagnose COVID-19 Using Machine Learning.Interdisciplinary sciences, computational life sciences · 2022Article
- Novel Crow Swarm Optimization Algorithm and Selection Approach for Optimal Deep Learning COVID-19 Diagnostic Model.Computational intelligence and neuroscience · 2022Article
- A Novel Benchmark Dataset for COVID-19 Detection during Third Wave in Pakistan.Computational intelligence and neuroscience · 2022Article
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Authors and funding
15 authors at 3 institutions in 1 country.
Funding
Abstract
Computed tomography (CT) is one of the most efficient diagnostic methods for rapid diagnosis of the widespread COVID-19. However, reading CT films brings a lot of concentration and time for doctors. Therefore, it is necessary to develop an automatic CT image diagnosis system to assist doctors in diagnosis. Previous studies devoted to COVID-19 in the past months focused mostly on discriminating COVID-19 infected patients from healthy persons and/or bacterial pneumonia patients, and have ignored typical viral pneumonia since it is hard to collect samples for viral pneumonia that is less frequent in adults. In addition, it is much more challenging to discriminate COVID-19 from typical viral pneumonia as COVID-19 is also a kind of virus. In this study, we have collected CT images of 262, 100, 219, and 78 persons for COVID-19, bacterial pneumonia, typical viral pneumonia, and healthy controls, respectively. To the best of our knowledge, this was the first study of quaternary classification to include also typical viral pneumonia. To effectively capture the subtle differences in CT images, we have constructed a new model by combining the ResNet50 backbone with SE blocks that was recently developed for fine image analysis. Our model was shown to outperform commonly used baseline models, achieving an overall accuracy of 0.94 with AUC of 0.96, recall of 0.94, precision of 0.95, and F1-score of 0.94. The model is available in https://github.com/Zhengfudan/COVID-19-Diagnosis-and-Pneumonia-Classification .
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