Automatic COVID-19 Detection from chest radiographic images using Convolutional Neural Network
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Abstract
The global pandemic of the novel coronavirus that started in Wuhan, China has affected more than 50 million people worldwide and caused more than 1263,787 tragic deaths. To date, the COVID-19 virus is still spreading and affecting thousands of people. The main problem with testing for COVID-19 is that there are very few test kits available for a large number of affected or suspicious individuals. This leads to the need for automatic detection systems that use artificial intelligence. Deep learning is one of the most powerful AI tools available, so we recommend creating a convolutional neural network to detect COVID-19 positive patients from chest radiographs. According to previous studies, lung X-rays of COVID-19-positive patients show obvious characteristics, so this is a reliable method for testing patients, because X-ray examination of suspicious patients is easier than rt-PCR. Our model has been trained with 820 chest radiographic images (excluding data augmentation) collected from 3 databases, with a classification accuracy of 99.45% (training accuracy of 99.70%), sensitivity of 99.30% and specificity of 99.40 %, proved that our model has become a reliable COVID-19 detector.
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SciScore for 10.1101/2020.11.08.20228080: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources In this section we will describe the datasets used, the architecture of the COV-19Net and the Implementation Procedure in the creation of the COV-19Net. 3.1 Collection of Dataset: For the training and evaluation of the COV-19Net we have collected chest radiographic images from 3 open source databases: 1) GitHub open source dataset by Chowdhury et al. [20] 2) Dataset from GitHub by Cohen et al. [21] 3) Positive radiographic images (CXR and CT) were carefully chosen from Italian Society of Medical and Interventional Radiology (SIRM) [22] COVID-19 DATABASE. COV-19Net.suggested: NoneResults …
SciScore for 10.1101/2020.11.08.20228080: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources In this section we will describe the datasets used, the architecture of the COV-19Net and the Implementation Procedure in the creation of the COV-19Net. 3.1 Collection of Dataset: For the training and evaluation of the COV-19Net we have collected chest radiographic images from 3 open source databases: 1) GitHub open source dataset by Chowdhury et al. [20] 2) Dataset from GitHub by Cohen et al. [21] 3) Positive radiographic images (CXR and CT) were carefully chosen from Italian Society of Medical and Interventional Radiology (SIRM) [22] COVID-19 DATABASE. COV-19Net.suggested: NoneResults 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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