Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia
We found that the real time reverse transcription-polymerase chain reaction (RT-PCR) detection of viral RNA from sputum or nasopharyngeal swab has a relatively low positive rate in the early stage to determine COVID-19 (named by the World Health Organization). The manifestations of computed tomography (CT) imaging of COVID-19 had their own characteristics, which are different from other types of viral pneumonia, such as Influenza-A viral pneumonia. Therefore, clinical doctors call for another early diagnostic criteria for this new type of pneumonia as soon as possible.This study aimed to establish an early screening model to distinguish COVID-19 pneumonia from Influenza-A viral pneumonia and healthy cases with pulmonary CT images using deep learning techniques. The candidate infection regions were first segmented out using a 3-dimensional deep learning model from pulmonary CT image set. These separated images were then categorized into COVID-19, Influenza-A viral pneumonia and irrelevant to infection groups, together with the corresponding confidence scores using a location-attention classification model. Finally the infection type and total confidence score of this CT case were calculated with Noisy-or Bayesian function.The experiments result of benchmark dataset showed that the overall accuracy was 86.7 % from the perspective of CT cases as a whole.The deep learning models established in this study were effective for the early screening of COVID-19 patients and demonstrated to be a promising supplementary diagnostic method for frontline clinical doctors..
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Preprint| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
arXiv.org - (2020) vom: 21. Feb. Zur Gesamtaufnahme - year:2020 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Xu, Xiaowei [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
XAR017275709 |
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| 520 | |a We found that the real time reverse transcription-polymerase chain reaction (RT-PCR) detection of viral RNA from sputum or nasopharyngeal swab has a relatively low positive rate in the early stage to determine COVID-19 (named by the World Health Organization). The manifestations of computed tomography (CT) imaging of COVID-19 had their own characteristics, which are different from other types of viral pneumonia, such as Influenza-A viral pneumonia. Therefore, clinical doctors call for another early diagnostic criteria for this new type of pneumonia as soon as possible.This study aimed to establish an early screening model to distinguish COVID-19 pneumonia from Influenza-A viral pneumonia and healthy cases with pulmonary CT images using deep learning techniques. The candidate infection regions were first segmented out using a 3-dimensional deep learning model from pulmonary CT image set. These separated images were then categorized into COVID-19, Influenza-A viral pneumonia and irrelevant to infection groups, together with the corresponding confidence scores using a location-attention classification model. Finally the infection type and total confidence score of this CT case were calculated with Noisy-or Bayesian function.The experiments result of benchmark dataset showed that the overall accuracy was 86.7 % from the perspective of CT cases as a whole.The deep learning models established in this study were effective for the early screening of COVID-19 patients and demonstrated to be a promising supplementary diagnostic method for frontline clinical doctors. | ||
| 700 | 1 | |a Xu, Xiaowei |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Xiangao |e verfasserin |4 aut | |
| 700 | 1 | |a Ma, Chunlian |e verfasserin |4 aut | |
| 700 | 1 | |a Du, Peng |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Xukun |e verfasserin |4 aut | |
| 700 | 1 | |a Lv, Shuangzhi |e verfasserin |4 aut | |
| 700 | 1 | |a Yu, Liang |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Yanfei |e verfasserin |4 aut | |
| 700 | 1 | |a Su, Junwei |e verfasserin |4 aut | |
| 700 | 1 | |a Lang, Guanjing |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Yongtao |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Hong |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Kaijin |e verfasserin |4 aut | |
| 700 | 1 | |a Ruan, Lingxiang |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Wei |e verfasserin |4 aut | |
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