COVID-Net, a Convolutional Neural Network design tailored for the detection of COVID-19 cases from chest radiography images.
The workflow expects as input a chest X-Ray image with the extension '.png', '.jpeg' or '.jpg' and then estimates the probability of having pneumonia, COVID-19, or no findings for chest X-Rays. The workflow produces a report showing the input image as well as the prediction.
The main component of the COVID_Net Workflow is the COVID-Net, one of the first open-source network designs for COVID-19 detection from CXR images. The network was trained with 16,756 chest radiography images across 13,645 patient cases from two open access data repositories.
Example of the output report generated by the workflow:
- Chest X-Ray: A Chest X-Ray image. Must be tagged as 'png' or 'jpeg'.
Output container files
- report.pdf: PDF report.
- report.txt: txt with prediction
- online_summary_report.html: Online report, visible in the second tab of "Show results".
- input image
COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest Radiography Images
- COVID-Net GitHub Repository
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