Abstract
Since December 2019, the novel coronavirus disease (COVID-19) caused by the syndrome coronavirus 2 (SARS-CoV-2) strain has spread widely around the world and has become a serious global public health problem. For this high-speed infectious disease, the application of X-ray to chest diagnosis plays a key role. In this study, we propose a chest X-ray image classification method based on feature fusion of a dense convolutional network (DenseNet) and a visual geometry group network (VGG16). This paper adds an attention mechanism (global attention machine block and category attention block) to the model to extract deep features. A residual network (ResNet) is used to segment effective image information to quickly achieve accurate classification. The average accuracy of our model in detecting binary classification can reach 98.0%. The average accuracy for three category classification can reach 97.3%. The experimental results show that the proposed model has good results in this work. Therefore, the use of deep learning and feature fusion technology in the classification of chest X-ray images can become an auxiliary tool for clinicians and radiologists.
【저자키워드】 COVID-19, image classification, feature fusion, VGG16, DenseNet, 【초록키워드】 SARS-CoV-2, coronavirus, deep learning, Diagnosis, Infectious disease, novel coronavirus disease, Spread, X-ray, Features, Accuracy, Chest, chest X-ray, information, mechanism, Radiologists, Chest X-ray image, clinician, global public health, average, syndrome, binary classification, effective, caused, auxiliary, 【제목키워드】 classification,