Computer aided diagnosis of COVID-19 is used to realize the intelligent image diagnosis,clinical diagnosis and clinical typing.In the process of auxiliary diagnosis of COVID-19,the obscure contrast between the focus area of the image and the tissue boundary,results in that the model can not well focus on the focus area as well as insufficient extraction of effective features.To solve these problems,a supplementary diagnosis model of COVID-19,seqAFF-ResNet was proposed herein.A serial attention fea-ture fusion module(seqAFF)was designed,which connected strip attentional feature fusion module(SAFF)and global local attentional feature fusion module(GLAFF)in series to obtain texture information as well as global and local information of the image to compensate the lack of detail feature extraction ability of convolutional neural network,so as to focus on the lesion area better.Deep and shallow feature fusion module(DSFF)was constructed using the semantic information of deep features to influ-ence the shallow information,meanwhile the spatial information of the shallow layer was passed into the deep features to fuse the deep and shallow features effectively.As a result,rich contextual information and achieving cross-layer attentional feature enhancement can be captured,enabling the network to better localize the lesion area.Compared with ResNet,the accuracy rate of seqAFFResNet increases by 3.42% ,the accuracy rate increases by 3.53% ,the F1 score increases by 2.77% ,and the AUC value increases by 0.9% .The experimental results show that the model in this paper can significantly improve the recogni-tion accuracy of COVID-19,exhibiting a better performance compared with similar models.The method proposed in this paper provides an effective identification method for the auxiliary diagnosis of COVID-19,and is of great significance for the computer-aided diagnosis of COVID-19.
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