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the COVID-19 pandemic outbreak, over 760 million confirmed cases and over 6.8 million deaths have been reported globally, according to the World Health Organization. While the SARS-CoV-2 virus carried by COVID-19 patients can be identified though the reverse transcription\u2013polymerase chain reaction (RT-PCR) test with high accuracy, clinical misdiagnosis between COVID-19 and pneumonia patients remains a challenge. Therefore, we developed a novel CovC-ReDRNet model to distinguish COVID-19 patients from pneumonia patients as well as normal cases. ResNet-18 was introduced as the backbone model and tailored for the feature representation afterward. In our feature-based randomized neural network (RNN) framework, the feature representation automatically pairs with the deep random vector function link network (dRVFL) as the optimal classifier, producing a CovC-ReDRNet model for the classification task. Results based on five-fold cross-validation reveal that our method achieved 94.94%, 97.01%, 97.56%, 96.81%, and 95.84% MA sensitivity, MA specificity, MA accuracy, MA precision, and MA F1-score, respectively. Ablation studies evidence the superiority of ResNet-18 over different backbone networks, RNNs over traditional classifiers, and deep RNNs over shallow RNNs. Moreover, our proposed model achieved a better MA accuracy than the state-of-the-art (SOTA) methods, the highest score of which was 95.57%. To conclude, our CovC-ReDRNet model could be perceived as an advanced computer-aided diagnostic model with high speed and high accuracy for classifying and predicting COVID-19 diseases.<\/jats:p>","DOI":"10.3390\/make5030037","type":"journal-article","created":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T00:50:52Z","timestamp":1687913452000},"page":"684-712","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["CovC-ReDRNet: A Deep Learning Model for COVID-19 Classification"],"prefix":"10.3390","volume":"5","author":[{"given":"Hanruo","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziquan","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4713-2791","authenticated-orcid":false,"given":"Shuihua","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4870-1493","authenticated-orcid":false,"given":"Yudong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK"},{"name":"School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000, China"},{"name":"Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,27]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2023). 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