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Engineering >> 2020, Volume 6, Issue 10 doi: 10.1016/j.eng.2020.04.010

A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia

a State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, China
b Department of Infectious Disease, Wenzhou Central Hospital, Wenzhou 325000, China
c Department of Infectious Disease, The First People's Hospital of Wenling, Wenling 317500, China
d Artificial Intelligence Lab, Hangzhou AiSmartVision Co., Ltd., Hangzhou 310012, China
e Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, China
f Department of Hepatobiliary and Pancreatic Surgery & Key Lab of Pancreatic Diseases Research of Zhejiang Province & The Innovation Centre for the Study of Pancreatic Diseases of Zhejiang Province & Clinical Medical Research Center of Hepatobiliary and Pancreatic Diseases in Zhejiang Province & Precision Innovation Center of the Diagnosis and Treatment of Hepatobiliary and Pancreatic Diseases of Zhejiang University, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, China

# These authors contributed equally to this work.

Received: 2020-03-23 Revised: 2020-04-21 Accepted: 2020-04-28 Available online: 2020-06-27

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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 pneumonia 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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