  The new type of coronavirus disease (COVID-19), which started in Wuhan, China
in December 2019, continues to spread rapidly affecting the whole world. It is
essential to have a highly sensitive diagnostic screening tool to detect the
disease as early as possible. Currently, chest CT imaging is preferred as the
primary screening tool for evaluating the COVID-19 pneumonia by radiological
imaging. However, CT imaging requires larger radiation doses, longer exposure
time, higher cost, and may suffer from patient movements. X-Ray imaging is a
fast, cheap, more patient-friendly and available in almost every healthcare
facility. Therefore, we have focused on X-Ray images and developed an
end-to-end deep learning model, i.e. Ensemble-CVDNet, to distinguish COVID-19
pneumonia from non-COVID pneumonia and healthy cases in this work. The proposed
model is based on a combination of three lightweight pre-trained models
SqueezeNet, ShuffleNet, and EfficientNet-B0 at different depths, and combines
feature maps in different abstraction levels. In the proposed end to-end model,
networks are used as feature extractors in parallel after fine-tuning, and some
additional layers are used at the top of them. The proposed model is evaluated
in the COVID-19 Radiography Database, a public data set consisting of 219
COVID-19, 1341 Healthy, and 1345 Viral Pneumonia chest X-Ray images.
Experimental results show that our lightweight Ensemble-CVDNet model provides
98.30% accuracy, 97.78% sensitivity, and 97.61% F1 score using only 5.62M
parameters. Moreover, it takes about 10ms to process and predict an X-Ray image
using the proposed method using a mid level GPU. We believe that the method
proposed in this study can be a helpful diagnostic screening tool for
radiologists in the early diagnosis of the disease.
