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Many researchers are studying novel Coronavirus pneumonia from chest X\u2010ray images. In order to improve the detection accuracy, two modules sensitive to feature information, dual\u2010path multiscale feature fusion module and dense depthwise separable convolution module, are proposed. Based on these two modules, a lightweight convolutional neural network model, D2\u2010CovidNet, is designed to assist experts in diagnosing COVID\u201019 by identifying chest X\u2010ray images. D2\u2010CovidNet is tested on two public data sets, and its classification accuracy, precision, sensitivity, specificity, and <jats:italic>F<\/jats:italic>1\u2010score are 94.56%, 95.14%, 94.02%, 96.61%, and 95.30%, respectively. Specifically, the precision, sensitivity, and specificity of the network for COVID\u201019 are 98.97%, 94.12%, and 99.84%, respectively. D2\u2010CovidNet has fewer computation number and parameter number. 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