Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia
- URL: http://arxiv.org/abs/2002.09334v1
- Date: Fri, 21 Feb 2020 14:44:21 GMT
- Title: Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia
- Authors: Xiaowei Xu, Xiangao Jiang, Chunlian Ma, Peng Du, Xukun Li, Shuangzhi
Lv, Liang Yu, Yanfei Chen, Junwei Su, Guanjing Lang, Yongtao Li, Hong Zhao,
Kaijin Xu, Lingxiang Ruan, Wei Wu
- Abstract summary: The manifestations of computed tomography (CT) imaging of COVID-19 had their own characteristics, which are different from other types of viral pneumonia, such as Influenza-A viral pneumonia.
This study aimed to establish an early screening model to distinguish COVID-19 pneumonia from Influenza-A viral pneumonia and healthy cases with pulmonary CT images using deep learning techniques.
- Score: 10.225348237731787
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We found that the real time reverse transcription-polymerase chain reaction
(RT-PCR) detection of viral RNA from sputum or nasopharyngeal swab has a
relatively low positive rate in the early stage to determine COVID-19 (named by
the World Health Organization). The manifestations of computed tomography (CT)
imaging of COVID-19 had their own characteristics, which are different from
other types of viral pneumonia, such as Influenza-A viral pneumonia. Therefore,
clinical doctors call for another early diagnostic criteria for this new type
of pneumonia as soon as possible.This study aimed to establish an early
screening model to distinguish COVID-19 pneumonia from Influenza-A viral
pneumonia and healthy cases with pulmonary CT images using deep learning
techniques. The candidate infection regions were first segmented out using a
3-dimensional deep learning model from pulmonary CT image set. These separated
images were then categorized into COVID-19, Influenza-A viral pneumonia and
irrelevant to infection groups, together with the corresponding confidence
scores using a location-attention classification model. Finally the infection
type and total confidence score of this CT case were calculated with Noisy-or
Bayesian function.The experiments result of benchmark dataset showed that the
overall accuracy was 86.7 % from the perspective of CT cases as a whole.The
deep learning models established in this study were effective for the early
screening of COVID-19 patients and demonstrated to be a promising supplementary
diagnostic method for frontline clinical doctors.
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