ABSTRACT
In recent years, with the pandemic of COVID-19, how to identify the positive cases of COVID-19 accurately and rapidly from patients has become the key to block the spread of the epidemic and assist clinical diagnosis. In this paper, a COVID-19 detection model was constructed for the purpose to identify the positive cases from patients with other lung diseases as well as the normal using the chest X-ray images. The basic structure of the detection system is a CNN model based on DesNet with some optimization algorithms and the accuracy has reached 94.2%. We also applied three multi-sample data augmentation methods: SMOTE, mixup and CutMix to the model to analyze their performance. By applying these methods, the model finally reached 97.9% on test set and showed a good generalization on other datasets, which could reach over 80% without extra training. The results show that using transfer learning and some muli-sample data augmentation methods can significantly improve the accuracy and overcome overfitting problem of fewshot learning, while others may not be so effective. © 2023 IEEE.