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

Abstract COVID-19 has caused over 6.35 million deaths and 555 confirmed cases till 11/July/2022. It a serious impact on individual health, social economic activities, other aspects. Based the gray-level co-occurrence matrix (GLCM), four-direction varying-distance GLCM (FDVD-GLCM) is presented. Afterward, five-property feature set (FPFS) extracts features from FDVD-GLCM. An extreme learning machine (ELM) used as classifier to recognize COVID-19. Our model finally dubbed FECNet. A multiple-way data augmentation method utilized boost training sets. Ten runs of tenfold cross-validation show that this FECNet achieves sensitivity 92.23 ± 2.14, specificity 93.18 0.87, precision 93.12 0.83, an accuracy 92.70 1.13 for first dataset, 92.19 1.89, 92.88 1.23, 92.83 1.22, 92.53 1.37 second dataset. We develop mobile app integrating model, web run cloud computing-based client–server modeled construction. This proposed corresponding effectively COVID-19, its performance better than five state-of-the-art recognition models.