Fusing global features and local information for COVID-19 detection with X-ray images

摘要

COVID-19 is a modern virus that has spread all over the world and is affecting billions of people. Timely and accurate detection is of great significance to slow down the spread of the virus and treat infected people effectively. Deep learning methods have been shown promising in COVID-19 detection due to their accurate feature extraction ability, which can enhance the classification ability of the detection model with high accuracy. Existing studies mostly focus on information compression and feature extraction, which inevitably leads to original information loss. This paper proposes a novel end-to-end COVID-19 classification model called MSCGRU, which is based on the fusion of multi-faceted global features with local information of original images. Firstly, multi-scale CNN is used to extract multi-scale global features from multiple channels, and these features are fused with local information to obtain comprehensive features. Secondly, GRU is adopted to extract deep abstract features from comprehensive features. The experimental results demonstrate that the proposed MSCGRU greatly strengthens the learning ability and achieves better prediction performance compared with other methods. The accuracy of multi-classification is 98.2%, and the accuracy of binary classification is 100%.

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