A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia

• DOI: 10.1016/j.eng.2020.04.010
• Publication Date: 2020-06-27T15:32:52Z
• Authors: Xiaowei Xu; Xiangao Jiang; Chunlian Ma; Peng Du; Xukun Li; Shuangzhi Lv; Liang Yu; Qin Ni; Yanfei Chen; Junwei Su; Guanjing Lang; Yongtao Li; Hong Zhao; Jun Liu; Kaijin Xu; Lingxiang Ruan; Jifang Sheng; Yunqing Qiu; Wei Wu; Tingbo Liang; Lanjuan Li
• Keywords: Viral Pneumonia; Coronavirus

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 coronavirus disease 2019 (COVID-19). Meanwhile, manifestations COVID-19 as seen through computed tomography (CT) imaging show individual characteristics that differ those other types pneumonia such influenza-A (IAVP). This study aimed to establish an screening model distinguish IAVP and healthy cases pulmonary CT images using deep learning techniques. A total 618 samples were collected: 219 110 patients with (mean age 50 years; 63 (57.3%) male patients); 224 61 156 (69.6%) 175 39 97 (55.4%) patients). All contributed three COVID-19-designated hospitals Zhejiang Province, China. First, candidate infection regions segmented out image set 3D model. These separated then categorized into COVID-19, IAVP, irrelevant (ITI) groups, together corresponding confidence scores, location-attention classification Finally, type overall score for each case calculated Noisy-OR Bayesian function. experimental result benchmark dataset showed accuracy was 86.7% terms all taken together. models established this effective demonstrated be promising supplementary diagnostic method frontline clinical doctors.