COVID-19 IDENTIFICATION IN CHEST X-RAY IMAGES ON FLAT AND HIERARCHICAL CLASSIFICATION SCENARIOS

The COVID-19 can cause severe pneumonia and is estimated to have a high impact on the healthcare system. The standard image diagnosis tests for pneumonia are chest X-ray (CXR) and computed tomography (CT) scan. CXR are useful in because it is cheaper, faster and more widespread than CT.
This study aims to identify pneumonia caused by COVID-19 from other types and also healthy lungs using only CXR images. In order to achieve the objectives, we have proposed a classification schema considering the multi-class and hierarchical perspectives, since pneumonia can be structured as a
hierarchy. Given the natural data imbalance in this domain, we also proposed the use of resampling algorithms in order to re-balance the classes distribution. O

COVID-19 IDENTIFICATION IN CHEST X-RAY IMAGES ON FLAT AND HIERARCHICAL CLASSIFICATION SCENARIOS
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