Automatic analysis system of COVID-19 radiographic lung images (XrayCoviDetector)
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Abstract
COVID-19 is a pandemic infectious disease caused by the SARS-CoV-2 virus, having reached more than 210 countries and territories. It produces symptoms such as fever, dry cough, dyspnea, fatigue, pneumonia, and radiological manifestations.
The most common reported RX and CT findings include lung consolidation and ground-glass opacities.
In this paper, we describe a machine learning-based system (XrayCoviDetector; until the image has a size www.covidetector.net ), that detects automatically, the probability that a thorax radiological image includes COVID-19 lung patterns.
XrayCoviDetector has an accuracy of 0.93, a sensitivity of 0.96, and a specificity of 0.90.
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SciScore for 10.1101/2020.08.20.20178723: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization Being a small data set, two sets were randomly formed. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
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 …
SciScore for 10.1101/2020.08.20.20178723: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization Being a small data set, two sets were randomly formed. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
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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