The Coronavirus disease 2019 (COVID19) pandemic has led to a dramatic loss of human life
worldwide and caused a tremendous challenge to public health. Immediate detection and
diagnosis of COVID19 have lifesaving importance for both patients and doctors. The
availability of COVID19 tests increased significantly in many countries, thereby provisioning
a limited availability of laboratory test kits additionally, the Reverse Transcription-Polymerase
Chain Reaction (RT-PCR) test for the diagnosis of COVID 19 is costly and time-consuming.
X-ray imaging is widely used for the diagnosis of COVID19. The detection of COVID19 based
on the manual investigation of X-ray images is a tedious process. Therefore, computer-aided
diagnosis (CAD) systems are needed for the automated detection of COVID19 disease. This
paper proposes a novel approach for the automated detection of COVID19 using chest X-ray
images. The Fixed Boundary-based Two-Dimensional Empirical Wavelet Transform (FB2
DEWT) is used to extract modes from the X-ray images. In our study, a single X-ray image is
decomposed into seven modes. The evaluated modes are used as input to the multiscale deep
Convolutional Neural Network (CNN) to classify X-ray images into no-finding, pneumonia,
and COVID19 classes. The proposed deep learning model is evaluated using the X-ray images
from two different publicly available databases, where database A consists of 1225 images and
database B consists of 9000 images.