COVID-19 X-ray Image Diagnostic with Deep Neural Networks

Abstract. The COVID-19 pandemic impacted all spheres of our society. The outbreak increased the pressure on public health systems, urging the scientific community to develop and evaluate methods to reliably diag- nose patients. Driven by their effectiveness in medical imaging analysis, deep neural networks have been seen as a possible alternative to automat- ically diagnose COVID-19 patients from chest X-rays. Despite promising initial results, most analyses so far have been performed in small and under-represented datasets. Considering this, in this work, we evaluate state-of-the-art convolutional neural network architectures proposed in recent years by the deep learning field on images from COVIDx [24], a dataset consisting of 13, 975 chest X-ray from COVID-19, pneumonia, and healthy patients. In our experiments, we investigate the effect of data pre-processing steps and class unbalancing for this task. Our best model, an ensemble of several networks, achieved an accuracy above 93% in the testing set, showing promising results in a challenging dataset. Keywords: COVID-19 diagnostic · Chest X-ray image analysis · Deep Learning.

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