  Amid the coronavirus disease(COVID-19) pandemic, humanity experiences a rapid
increase in infection numbers across the world. Challenge hospitals are faced
with, in the fight against the virus, is the effective screening of incoming
patients. One methodology is the assessment of chest radiography(CXR) images,
which usually requires expert radiologist's knowledge. In this paper, we
propose an explainable deep neural networks(DNN)-based method for automatic
detection of COVID-19 symptoms from CXR images, which we call
DeepCOVIDExplainer. We used 15,959 CXR images of 15,854 patients, covering
normal, pneumonia, and COVID-19 cases. CXR images are first comprehensively
preprocessed, before being augmented and classified with a neural ensemble
method, followed by highlighting class-discriminating regions using
gradient-guided class activation maps(Grad-CAM++) and layer-wise relevance
propagation(LRP). Further, we provide human-interpretable explanations of the
predictions. Evaluation results based on hold-out data show that our approach
can identify COVID-19 confidently with a positive predictive value(PPV) of
91.6%, 92.45%, and 96.12%; precision, recall, and F1 score of 94.6%, 94.3%, and
94.6%, respectively for normal, pneumonia, and COVID-19 cases, respectively,
making it comparable or improved results over recent approaches. We hope that
our findings will be a useful contribution to the fight against COVID-19 and,
in more general, towards an increasing acceptance and adoption of AI-assisted
applications in the clinical practice.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning