Since the outbreak of Coronavirus disease 2019 (COVID-19), it has been spreading rapidly worldwide and has not yet been effectively controlled. Many researchers are studying novel Coronavirus pneumonia from chest X-ray images. In order to improve the detection accuracy, two modules sensitive to feature information, dual-path multiscale feature fusion module and dense depthwise separable convolution module, are proposed. Based on these two modules, a lightweight convolutional neural network model, D2-CovidNet, is designed to assist experts in diagnosing COVID-19 by identifying chest X-ray images. D2-CovidNet is tested on two public data sets, and its classification accuracy, precision, sensitivity, specificity, and F 1-score are 94.56%, 95.14%, 94.02%, 96.61%, and 95.30%, respectively. Specifically, the precision, sensitivity, and specificity of the network for COVID-19 are 98.97%, 94.12%, and 99.84%, respectively. D2-CovidNet has fewer computation number and parameter number. Compared with other methods, D2-CovidNet can help diagnose COVID-19 more quickly and accurately.
【초록키워드】 COVID-19, coronavirus disease, Coronavirus disease 2019, Coronavirus pneumonia, Pneumonia, novel coronavirus pneumonia, Novel coronavirus, sensitivity, specificity, Accuracy, outbreak, Convolutional neural network, chest X-ray, network, information, diagnose, Chest X-ray images, Convolution, computation, data sets, network model, Precision, help, diagnosing, parameter, researcher, IMPROVE, tested, assist, effectively controlled, 【제목키워드】 COVID-19, detection, learning, Model, Image, deep,