A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia

By Xiaowei Xu, Xiangao Jiang, Chunlian Ma, P. Du, Xukun Li, Shuangzhi Lv, Liang Yu, Qin Ni, Yanfei Chen, Jun-wei Su and 11 more. Engineering, 2020

841 citations recorded by the source corpus.

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.

Subfields: Infectious Diseases, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/32eabaa9c724c68a9cb4a72022a35e75f21256b3

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In Infectious Diseases

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest Radiography Images (2020, background): “Motivated by the need for faster interpretation of radiography images, a number of artificial intelligence (AI) systems based on deep learning [7] have been proposed and results have shown to be quite promising in terms of accuracy in detecting patients infected with COVID-19 via radiography imaging”
Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning (2020, methodology): “Initially, Real Time Reverse Transcription Polymerase Chain Reaction (RT-PCR) is the only technique to detect the COVID-19 from respiratory samplings (Wang, Xu et al., 2020).”
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Using X-ray images and deep learning for automated detection of coronavirus disease (2020, methodology): “Hence, various studies have demonstrated the capacity of neural systems, particularly convolutional neural systems to precisely recognize the presence of Pneumonia (Gozes et al., 2020; Xu et al., 2020).”
COVID-CT-Dataset: A CT Scan Dataset about COVID-19 (2020, background): “There have been several works (Li et al., 2020; Wang et al., 2020; Xu et al., 2020) studying the effectiveness of CT scans in screening and testing COVID-19 and the results are promising.”
Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification From CT Images (2020, methodology): “Besides, deep learning algorithms have been proposed for COVID-19monitoring [13], screening [14] and prediction of the hospital stay [15].”
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COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images (2020, background): “A number of studies have proposed deep learning systems based on chest CT imaging to distinguish COVID-19 cases from non-COVID-19 cases (which may include both normal and abnormal cases) [16, 20, 21, 22, 23, 24, 25, 26, 27, 17, 28, 29, 30].”
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AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app (2020, methodology): “To this end, the medical researcher in our team began by analyzing in-depth the pathomorphological changes caused by COVID-19 in respiratory system by examining the data reported in numerous recent X-rays [13], [18]–[20] and CTscans based studies [14], [21], [22] of COVID-19 patients.”