About: This paper reports the development and web deployment of an inference model for Coronavirus COVID-19 using machine vision on chest radiographs (CXR). The transfer learning from the Residual Network (RESNET-50) was leveraged for model development on CXR images from healthy individuals, bacterial and viral pneumonia, and COVID-19 positives patients. The performance metrics showed an accuracy of 99%, a recall valued of 99.8%, a precision of 99% and an F1 score of 99.8% for COVID-19 inference. The model was further successfully validated on CXR images from an independent repository. The implemented model was deployed with a web graphical user interface for inference (https://medics-inference.onrender.com ) for the medical research community; an associated cron job is scheduled to continue the learning process when novel and validated information becomes available   Goto Sponge  NotDistinct  Permalink

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  • This paper reports the development and web deployment of an inference model for Coronavirus COVID-19 using machine vision on chest radiographs (CXR). The transfer learning from the Residual Network (RESNET-50) was leveraged for model development on CXR images from healthy individuals, bacterial and viral pneumonia, and COVID-19 positives patients. The performance metrics showed an accuracy of 99%, a recall valued of 99.8%, a precision of 99% and an F1 score of 99.8% for COVID-19 inference. The model was further successfully validated on CXR images from an independent repository. The implemented model was deployed with a web graphical user interface for inference (https://medics-inference.onrender.com ) for the medical research community; an associated cron job is scheduled to continue the learning process when novel and validated information becomes available
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  • Logical consequence
  • Software architecture
  • Unix SUS2008 utilities
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