COVID-19 detection from chest X-Ray images using Deep Learning and Convolutional Neural Networks

  1. Antonios Makris
  2. Ioannis Kontopoulos
  3. Konstantinos Tserpes

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Abstract

The COVID-19 pandemic in 2020 has highlighted the need to pull all available resources towards the mitigation of the devastating effects of such “Black Swan” events. Towards that end, we investigated the option to employ technology in order to assist the diagnosis of patients infected by the virus. As such, several state-of-the-art pre-trained convolutional neural networks were evaluated as of their ability to detect infected patients from chest X-Ray images. A dataset was created as a mix of publicly available X-ray images from patients with confirmed COVID-19 disease, common bacterial pneumonia and healthy individuals. To mitigate the small number of samples, we employed transfer learning, which transfers knowledge extracted by pre-trained models to the model to be trained. The experimental results demonstrate that the classification performance can reach an accuracy of 95% for the best two models.

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    SciScore for 10.1101/2020.05.22.20110817: (What is this?)

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  2. Version published to 10.1101/2020.05.22.20110817v1 on medRxiv