Abstract: Aiming at the problems of a certain false negative rate and long time consumption in the detection of novel coronavirus pneumonia (COVID-19) by reverse transcription polymerase chain reaction,this paper proposes a multi-channel dual attention network (MDA-Net) based on deep transfer learning to detect lung images.Firstly,under the framework of deep transfer learning,a multi-channel dual attention module is introduced,which utilizes the positional relationship of multiple channels to fuse image features of different scales.Then,the attention mechanism is combined with a lightweight convolutional neural network to expand the MDA-Net receptive field and improve the feature extraction ability of complex and edge regions of the images.Finally,the MDA-Net is tested on different datasets,and the binary-classification task and three-classification task can achieve an average accuracy of 99.25% and 99.39% respectively,showing good classification performance.