M3 Res-Transformer:Chest X-ray Image Recognition Model of COVID-19
COVID-19 has seriously affected human life and health since its outbreak.In recent years,residual neural network has been widely used in COVID-19 recognition task to assist doctors to quickly diagnose COVID-19 patients.However,the shape of COVID-19 image lesion regions is complex,the size is different,and the boundary with surrounding tissues is blurred,which make it difficult for the network to extract effective features.Aiming at the above problems,a M3 Res-Transformer model for COVID-19 Chest X-ray image recognition is proposed.Res-Transformer is used as the back-bone network of the model,combining ResNet and ViT to effectively integrate local lesion features and global features;A mixed residual attention module(mraM)is designed to enhance the feature expression ability of the network by considering the interdependence of channels and spatial locations;In order to increase the receptive field and extract multi-scale fea-tures,the multi-scale dilated residual module(mdrM)is constructed by superimposing dilated convolution with different di-lation rates,and three mdrM with gradually shrinking scales are used for multi-scale feature extraction according to the dif-ference of feature scales at different layers;The contextual cross-awareness module(ccaM)is proposed,which uses the se-mantic information of deep features to guide shallow features,then embeds the spatial information of shallow features into deep features,and uses the cross-weighted attention mechanism to efficiently aggregate deep and shallow features to obtain richer contextual information.In order to verify the effectiveness of the model in this paper,experiments were conducted on the Chest X-ray image dataset of COVID-19,and through comparison with advanced CNN classification models,com-parison with ResNet50 models fusing different attention mechanisms,comparison with Transformer-based classification models and ablation experiment,the results showed that the Acc,Pre,Rec,F1-Score and Spe indexes of the proposed model are 96.33%,96.36%,96.33%,96.35%and 96.26%respectively,which effectively improves the recognition accuracy in CO-VID-19 Chest X-ray image recognition task,then it is further verified by visualization method,which provides important reference value for COVID-19 aided diagnosis.
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