Abstract: In response to many problems such as the rapid spread of new coronary pneumonia， the time-consuming process of manual diagnosis using COVID-19 medical images， the imbalance of medical resources and the pressure of doctors’ diagnosis， this paper introduces a new attention module ECBAM on the basis of the lightweight network EfficientNet-B0 and proposes the EfficientNet-ECBAM network. Firstly， replacing the SE module in the EfficientNet-B0 network structure with this module can improve the problem that some details of the SE module are lost due to the downscaling operation. Secondly， because the ECBAM module can extract features in both channel and space dimensions， it can also improve the problem that the SE module extracts insufficient information of image features. On the selected COVID-19 chest X-ray dataset， compared with the classical convolutional neural classification network VGG16 and ResNet-50， the accuracy of the improved EfficientNet-ECBAM network based on the EfficientNet-B0 network is improved by 3.76 percentage points and 2.13 percentage points respectively， specificity and sensitivity are also improved. The number of model parameters is also reduced by 97.3% and 85.6% respectively. Compared with the lightweight network SqueezeNet and MobileNet V1， the accuracy of EfficientNet-ECBAM is improved by 2.97 percentage points and 2.44 percentage points respectively. The improved ECBAM module also outperforms other attention modules in the ablation experiments in all metrics. The experimental results show that the EfficientNet-ECBAM network model proposed in this paper has the advantages of good classification performance， low number of parameters and low computation， which is favorable for deployment in medical institutions in less economically developed areas.

Key words: COVID-19, deep learning, EfficientNet, attention module