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A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia
Engineering ( IF 10.1 ) Pub Date : 2020-10-01 , DOI: 10.1016/j.eng.2020.04.010 Xiaowei Xu 1 , Xiangao Jiang 2 , Chunlian Ma 3 , Peng Du 4 , Xukun Li 4 , Shuangzhi Lv 5 , Liang Yu 1 , Qin Ni 1 , Yanfei Chen 1 , Junwei Su 1 , Guanjing Lang 1 , Yongtao Li 1 , Hong Zhao 1 , Jun Liu 1 , Kaijin Xu 1 , Lingxiang Ruan 5 , Jifang Sheng 1 , Yunqing Qiu 1 , Wei Wu 1 , Tingbo Liang 6 , Lanjuan Li 1
Engineering ( IF 10.1 ) Pub Date : 2020-10-01 , DOI: 10.1016/j.eng.2020.04.010 Xiaowei Xu 1 , Xiangao Jiang 2 , Chunlian Ma 3 , Peng Du 4 , Xukun Li 4 , Shuangzhi Lv 5 , Liang Yu 1 , Qin Ni 1 , Yanfei Chen 1 , Junwei Su 1 , Guanjing Lang 1 , Yongtao Li 1 , Hong Zhao 1 , Jun Liu 1 , Kaijin Xu 1 , Lingxiang Ruan 5 , Jifang Sheng 1 , Yunqing Qiu 1 , Wei Wu 1 , Tingbo Liang 6 , Lanjuan Li 1
Affiliation
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 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.
更新日期：2020-10-01
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