The use of Efficientnet_b0 network to identify COVID-19 in chest X-ray images

Abstract

Abstract A newly discovered coronavirus called COVID-19 poses the greatest threat to mankind in the twenty-first century. Mortality has dramatically increased in all cities and countries due to the virus's current rate of spread. A speedy and precise diagnosis is also necessary in order to treat the illness. This study identified three groups for chest X-ray images: Covid, normal, and pneumonia. This study's objective is to present a framework for categorizing chest X-ray images into three groups of pneumonia, normal, and Covid scenarios. To do this, chest X-ray images from the Kaggle database which have been utilized in previous studies were obtained. It is suggested to use an Efficientnet_b0 model to identify characteristics in raw data hierarchically. An unedited X-ray image of the chest is enhanced for more reasonable assumptions in order to apply the proposed method in real-world situations. With an overall accuracy of 93.75%, the proposed network correctly identified the chest X-ray images to the classes of Covid, viral pneumonia, and normal on the test set. 90% accuracy rate for the test dataset was attained for the viral pneumonitis group. On the test dataset, the Normal class accuracy was 94.7%, while the Covid class accuracy was 96%. The findings indicate that the network is robust. In addition, when compared to the most advanced techniques of identifying pneumonia, the concluded findings from the suggested model are highly encouraging. Since the recommended network is successful at doing so utilizing chest X-ray imaging, radiologists can diagnose COVID-19 and other lung infectious infections promptly and correctly.

Keywords

• Coronavirus
• X-ray
• Convolutional neural network
• Efficientnet_b0