  The COVID-19 pandemic continues to severely undermine the prosperity of the
global health system. To combat this pandemic, effective screening techniques
for infected patients are indispensable. There is no doubt that the use of
chest X-ray images for radiological assessment is one of the essential
screening techniques. Some of the early studies revealed that the patient's
chest X-ray images showed abnormalities, which is natural for patients infected
with COVID-19. In this paper, we proposed a parallel-dilated convolutional
neural network (CNN) based COVID-19 detection system from chest x-ray images,
named as Parallel-Dilated COVIDNet (PDCOVIDNet). First, the publicly available
chest X-ray collection fully preloaded and enhanced, and then classified by the
proposed method. Differing convolution dilation rate in a parallel form
demonstrates the proof-of-principle for using PDCOVIDNet to extract
radiological features for COVID-19 detection. Accordingly, we have assisted our
method with two visualization methods, which are specifically designed to
increase understanding of the key components associated with COVID-19
infection. Both visualization methods compute gradients for a given image
category related to feature maps of the last convolutional layer to create a
class-discriminative region. In our experiment, we used a total of 2,905 chest
X-ray images, comprising three cases (such as COVID-19, normal, and viral
pneumonia), and empirical evaluations revealed that the proposed method
extracted more significant features expeditiously related to the suspected
disease. The experimental results demonstrate that our proposed method
significantly improves performance metrics: accuracy, precision, recall, and F1
scores reach 96.58%, 96.58%, 96.59%, and 96.58%, respectively, which is
comparable or enhanced compared with the state-of-the-art methods.
