A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia
© 2020 THE AUTHORS..
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.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2020 |
|---|---|
| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:6 |
|---|---|
| Enthalten in: |
Engineering (Beijing, China) - 6(2020), 10 vom: 28. Okt., Seite 1122-1129 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Xu, Xiaowei [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
COVID-19 |
|---|
| Anmerkungen: |
Date Revised 08.12.2020 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.1016/j.eng.2020.04.010 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM31411193X |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM31411193X | ||
| 003 | DE-627 | ||
| 005 | 20231225152032.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2020 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.eng.2020.04.010 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1047.xml |
| 035 | |a (DE-627)NLM31411193X | ||
| 035 | |a (NLM)32837749 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Xu, Xiaowei |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia |
| 264 | 1 | |c 2020 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 08.12.2020 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © 2020 THE AUTHORS. | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a Computed tomography | |
| 650 | 4 | |a Location-attention classification model | |
| 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 Ni, Qin |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 Liu, Jun |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 Sheng, Jifang |e verfasserin |4 aut | |
| 700 | 1 | |a Qiu, Yunqing |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Liang, Tingbo |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Lanjuan |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Engineering (Beijing, China) |d 2015 |g 6(2020), 10 vom: 28. Okt., Seite 1122-1129 |w (DE-627)NLM25591248X |x 2095-8099 |7 nnns |
| 773 | 1 | 8 | |g volume:6 |g year:2020 |g number:10 |g day:28 |g month:10 |g pages:1122-1129 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1016/j.eng.2020.04.010 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 6 |j 2020 |e 10 |b 28 |c 10 |h 1122-1129 | ||