Classification of X-ray images of COVID-19 based on CNN and improved swin transformer model

Faced with the rapid spread of COVID-19, nucleic acid testing methods can detect positive cases relatively quickly. Still, the time-consuming detection and frequent false-negative issues have led to a sharp increase in the demand for alternative diagnostic tools for COVID-19. In this paper, using medical imaging technology and deep learning technology, a model combining a convolutional neural network and an improved Swin Transformer network is designed to detect chest X-ray images of COVID-19. The image is input into the convolutional layer to extract the local details of the image. Then, to solve the problem that some heads do not play a role in calculating multi-head self-attention due to too small a weight, a learnable bias parameter is added to each individual computing head to enhance the specific weight of each head. Experiments show that this method has a recognition rate of 98.25% for chest X-ray images of COVID-19. Indicators such as recall rate and F1 score have been improved compared with some current methods.