A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia
Open accessAbstract
The real-time reverse transcription-polymerase chain reaction (RT-PCR) detection of viral RNA from sputum or nasopharyngeal swab had a relatively low positive rate in the early stage of coronavirus disease 2019 (COVID-19). Meanwhile, the manifestations of COVID-19 as seen through computed tomography (CT) imaging show individual characteristics that differ from those of other types of viral pneumonia such as influenza-A viral pneumonia (IAVP). This study aimed to establish an early screening model to distinguish COVID-19 from IAVP and healthy cases through pulmonary CT images using deep learning techniques. A total of 618 CT samples were collected: 219 samples from 110 patients with COVID-19 (mean age 50 years; 63 (57.3%) male patients); 224 samples from 224 patients with IAVP (mean age 61 years; 156 (69.6%) male patients); and 175 samples from 175 healthy cases (mean age 39 years; 97 (55.4%) male patients). All CT samples were contributed from three COVID-19-designated hospitals in Zhejiang Province, China. First, the candidate infection regions were segmented out from the pulmonary CT image set using a 3D deep learning model. These separated images were then categorized into the COVID-19, IAVP, and irrelevant to infection (ITI) groups, together with the corresponding confidence scores, using a location-attention classification model. Finally, the infection type and overall confidence score for each CT case were calculated using the Noisy-OR Bayesian function. The experimental result of the benchmark dataset showed that the overall accuracy rate was 86.7% in terms of all the CT cases taken together. The deep learning models established in this study were effective for the early screening of COVID-19 patients and were demonstrated to be a promising supplementary diagnostic method for frontline clinical doctors.
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A 3D DL-based location-attention classification model using pulmonary CT images showed high accuracy of 86.7% for classifying COVID-19, Influenza-A viral pneumonia, and healthy cases.
“Using these features, a 3D DL-based a location-attention classification model is developed using a pulmonary CT image set for symptom classification, showing a high accuracy with 86.7% for three groups: COVID-19, Influenza-A viral pneumonia and healthy cases.”
From Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey (2021) · cites this paper as [112]
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- Authors
- Xu, X., Jiang, X., Ma, C., Du, P., Li, X., Lv, S., Yu, L., Ni, Q., Chen, Y., Su, J., Lang, G., Li, Y., Zhao, H., Liu, J., Xu, K., Ruan, L., Sheng, J., Qiu, Y., Wu, W., Liang, T., & Li, L.
- Affiliations
- State Key Laboratory of Diagnosis and Treatment of Infectious Diseases (CN)
- Wenzhou Central Hospital (CN)
- The First People's Hospital of Wenling (CN)
- First Affiliated Hospital Zhejiang University (CN)
- Corresponding author
- Wu, Wei
- Liang, Tingbo
- Venue
- Engineering
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 6
- Issue
- 10
- Pages
- 1122-1129
- ISSN
- 2095-8099 (ISSN-L)
- PMC
- PMC7320702
- OpenAlex
- W3007170347
- Cited by
- 1,209
- References
- 5
- Citations by year
- 2026: 15 · 2025: 49 · 2024: 89 · 2023: 165 · 2022: 290 · 2021: 421 · 2020: 180
- FWCI
- 128.58
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Engineering (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Funders
- National Major Science and Technology Projects of China
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- COVID-19 pandemic, CT image classification, pneumonia screening, early diagnosis, deep learning
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