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 ac...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%.展开更多
基金This work was supported by the Research Center of the College of Computer and Information Sciences at King Saud University.The authors are grateful for this support.
文摘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%.