With the spread of COVID-19 around the globe over the past year, the usage of
artificial intelligence (AI) algorithms and image processing methods to analyze
the X-ray images of patients' chest with COVID-19 has become essential. The
COVID-19 virus recognition in the lung area of a patient is one of the basic
and essential needs of clicical centers and hospitals. Most research in this
field has been devoted to papers on the basis of deep learning methods
utilizing CNNs (Convolutional Neural Network), which mainly deal with the
screening of sick and healthy people.In this study, a new structure of a
19-layer CNN has been recommended for accurately recognition of the COVID-19
from the X-ray pictures of chest. The offered CNN is developed to serve as a
precise diagnosis system for a three class (viral pneumonia, Normal, COVID) and
a four classclassification (Lung opacity, Normal, COVID-19, and pneumonia). A
comparison is conducted among the outcomes of the offered procedure and some
popular pretrained networks, including Inception, Alexnet, ResNet50,
Squeezenet, and VGG19 and based on Specificity, Accuracy, Precision,
Sensitivity, Confusion Matrix, and F1-score. The experimental results of the
offered CNN method specify its dominance over the existing published
procedures. This method can be a useful tool for clinicians in deciding
properly about COVID-19