Differentiating COVID-19 from other types of pneumonia with convolutional neural networks
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Abstract
INTRODUCTION
A widely-used method for diagnosing COVID-19 is the nucleic acid test based on real-time reverse transcriptase-polymerase chain reaction (RT-PCR). However, the sensitivity of real time RT-PCR tests is low and it can take up to 8 hours to receive the test results. Radiologic methods can provide higher sensitivity. The aim of this study is to investigate the use of X-ray and convolutional neural networks for the diagnosis of COVID-19 and to differentiate it from viral and/or bacterial pneumonia, as 2-class (bacterial pneumonia vs COVID-19 and viral pneumonia vs COVID-19) and 3- class (bacterial pneumonia, COVID-19, and healthy group (BCH), and among viral pneumonia, COVID- 19, and healthy group (VCH)) experiments.
METHODS
225 COVID-19, 1,583 healthy control, 2,780 bacterial pneumonia, and 1,493 viral pneumonia chest X-ray images were used. 2-class- and 3-class-experiments were performed with different convolutional neural network (ConvNet) architectures, with different variations of convolutional layers and fully-connected layers.
RESULTS
The results showed that bacterial pneumonia vs COVID-19 and viral pneumonia vs COVID- 19 reached a mean ROC AUC of 97.32% and 96.80%, respectively. In the 3-class-experiments, macro-average F1 scores of 95.79% and 94.59% were obtained in terms of detecting COVID-19 among BCH and VCH, respectively.
CONCLUSIONS
The ConvNet was able to distinguish the COVID-19 images among non-COVID-19 images, namely bacterial and viral pneumonia as well as normal X-ray images.
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SciScore for 10.1101/2020.05.26.20113761: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IRB: The project built for creating this dataset was approved by the University of Montreal’s Ethics Committee
Consent: Informed consent was obtained for all subjects, and the study was approved by the relevant institutional review board at each data acquisition site.Randomization A total of 15 images randomly selected from all classes were assigned as the validation set. Blinding not detected. Power Analysis not detected. Sex as a biological variable Based on the provided information, there were 131 male patients and 64 female patients, and the average age for the COVID-19 group was 58.8±14.9 years. 1,583 healthy control, 2,780 bacterial pneumonia, and 1,493 viral … SciScore for 10.1101/2020.05.26.20113761: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IRB: The project built for creating this dataset was approved by the University of Montreal’s Ethics Committee
Consent: Informed consent was obtained for all subjects, and the study was approved by the relevant institutional review board at each data acquisition site.Randomization A total of 15 images randomly selected from all classes were assigned as the validation set. Blinding not detected. Power Analysis not detected. Sex as a biological variable Based on the provided information, there were 131 male patients and 64 female patients, and the average age for the COVID-19 group was 58.8±14.9 years. 1,583 healthy control, 2,780 bacterial pneumonia, and 1,493 viral pneumonia chest X-ray images were obtained from [17]. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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