Research on ARS-CNN algorithm in the identification of COVID-19

ABSTRACT

With the spread of new coronary pneumonia, in order to accurately diagnose COVID-19, this article proposes an improved new coronary pneumonia recognition algorithm based on convolutional neural network, namely the ARS-CNN algorithm. Based on the CNN network structure, this algorithm adds new functional modules. Firstly, in order to capture the multi-scale feature information of different receptive fields and strengthen the network's use of image features, a jump connection RFB structure is proposed. Secondly, the problem of local information loss caused by the reduction of image resolution during the feature extraction process of the network is improved by short-connecting the aspp module. Finally, the attention mechanism GC module and the sSE module are merged to achieve the screening of feature information and the interaction between feature information, thereby improving the accuracy of new coronary pneumonia recognition. Experiments on the public COVID-19 Chest X-ray Database data set show that the weighted average accuracy, precision, recall, FI score, and specificity of the algorithm proposed in this article are 98.22%, 97.91%, 97.95%, 97.92%, 98.33%, respectively. Compared with other classification algorithms, the algorithm proposed in this paper can efficiently recognize lung diseases and has higher recognition performance.