A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia. (October 2020)

• Record Type: Journal Article
• Authors: Xu, Xiaowei; Jiang, Xiangao; Ma, Chunlian; Du, Peng; Li, Xukun; Lv, Shuangzhi; Yu, Liang; Ni, Qin; Chen, Yanfei; Su, Junwei; Lang, Guanjing; Li, Yongtao; Zhao, Hong; Liu, Jun; Xu, Kaijin; Ruan, Lingxiang; Sheng, Jifang; Qiu, Yunqing; Wu, Wei; Liang, Tingbo; Li, Lanjuan
• Is Part Of: Engineering. Volume 6:Number 10(2020)
• Page Start: 1122
• Page End: 1129
• Publication Date: 2020-10
• Subjects: COVID-19 -- Location-attention classification model -- Computed tomography; Engineering -- Periodicals; Engineering -- China -- Periodicals; 620.005
• DOI: 10.1016/j.eng.2020.04.010
• Languages: English
• ISSNs: 2095-8099

Abstract

Abstract: 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.