FECNet: a Neural Network and a Mobile App for COVID-19 Recognition

COVID-19 has caused over 6.35 million deaths and over 555 million confirmed cases till 11/July/2022. 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 & PLUSMN; 2.14, a specificity of 93.18 & PLUSMN; 0.87, a precision of 93.12 & PLUSMN; 0.83, and an accuracy of 92.70 & PLUSMN; 1.13 for the first dataset, and a sensitivity of 92.19 & PLUSMN; 1.89, a specificity of 92.88 & PLUSMN; 1.23, a precision of 92.83 & PLUSMN; 1.22, and an accuracy of 92.53 & PLUSMN; 1.37 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-server modeled construction. This proposed FECNet and the corresponding mobile app effectively recognize COVID-19, and its performance is better than five state-of-the-art COVID-19 recognition models.