The world has been suffering from the Coronavirus(COVID-19)pandemic since its appearance in late 2019.COVID-19 spread has led to a drastic increase of the number of infected people and deaths worldwide.Imminent and accurate diagnosis of positive cases emerged as a natural alternative to reduce the number of serious infections and limit the spread of the disease.In this paper,we proposed an X-ray based COVID-19 classification system that aims at diagnosing positive COVID-19 cases.Specifically,we adapted lightweight versions of EfficientNet as backbone of the proposed recognition system.Particularly,lightweight EfficientNet networks were used to build classification models able to discriminate between positive and negative COVID-19 cases using chest X-ray images.The proposed models ensure a trade-off between scaling down the architecture of the deep network to reduce the computational cost and optimizing the classification performance.In the experiments,a public dataset containing 7,345 chest X-ray images was used to train,validate and test the proposed models for binary and multiclass classification problems,respectively.The obtained results showed the EfficientNet-elite-B9-V2,which is the lightest proposed model yielded an accuracy of 96%.On the other hand,EfficientNet-lite-B0 overtook the other models,and achieved an accuracy of 99%.
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