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It has caused a serious impact on individual health, social and economic activities, and other aspects. Based on the gray-level co-occurrence matrix (GLCM), a four-direction varying-distance GLCM (FDVD-GLCM) is presented. Afterward, a five-property feature set (FPFS) extracts features from FDVD-GLCM. An extreme learning machine (ELM) is used as the classifier to recognize COVID-19. Our model is finally dubbed FECNet. A multiple-way data augmentation method is utilized to boost the training sets. Ten runs of tenfold cross-validation show that this FECNet model achieves a sensitivity of 92.23\u2009\u00b1\u20092.14, a specificity of 93.18\u2009\u00b1\u20090.87, a precision of 93.12\u2009\u00b1\u20090.83, and an accuracy of 92.70\u2009\u00b1\u20091.13 for the first dataset, and a sensitivity of 92.19\u2009\u00b1\u20091.89, a specificity of 92.88\u2009\u00b1\u20091.23, a precision of 92.83\u2009\u00b1\u20091.22, and an accuracy of 92.53\u2009\u00b1\u20091.37 for the second dataset. We develop a mobile app integrating the FECNet model, and this web app is run on a cloud computing-based client\u2013server modeled construction. 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