{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T03:22:35Z","timestamp":1765423355330,"version":"3.37.3"},"reference-count":30,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,8,11]],"date-time":"2022-08-11T00:00:00Z","timestamp":1660176000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002383","name":"King Saud University","doi-asserted-by":"publisher","award":["RSP-2022R426"],"award-info":[{"award-number":["RSP-2022R426"]}],"id":[{"id":"10.13039\/501100002383","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2022,8,11]]},"abstract":"<jats:p>The COVID-19 infection is the greatest danger to humankind right now because of the devastation it causes to the lives of its victims. It is important that infected people be tested in a timely manner in order to halt the spread of the disease. Physical approaches are time-consuming, expensive, and tedious. As a result, there is a pressing need for a cost-effective and efficient automated tool. A convolutional neural network is presented in this paper for analysing X-ray pictures of patients\u2019 chests. For the analysis of COVID-19 infections, this study investigates the most suitable pretrained deep learning models, which can be integrated with mobile or online apps and support the mobility of diagnostic instruments in the form of a portable tool. Patients can use the smartphone app to find the nearest healthcare testing facility, book an appointment, and get instantaneous results, while healthcare professionals can keep track of the details thanks to the web and mobile applications built for this study. Medical practitioners can apply the COVID-19 detection model for chest frontal X-ray pictures with ease. A user-friendly interface is created to make our end-to-end solution paradigm work. Based on the data, it appears that the model could be useful in the real world.<\/jats:p>","DOI":"10.1155\/2022\/7126259","type":"journal-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T01:20:09Z","timestamp":1660267209000},"page":"1-14","source":"Crossref","is-referenced-by-count":12,"title":["An Effective Deep Learning Model for Health Monitoring and Detection of COVID-19 Infected Patients: An End-to-End Solution"],"prefix":"10.1155","volume":"2022","author":[{"given":"Vidyadevi G.","family":"Biradar","sequence":"first","affiliation":[{"name":"Department of Information Science and Engineering, Nitte Meenakshi Institute of Technology, Bangalore, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mejdal A.","family":"Alqahtani","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, King Saud University, Riyadh, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"H. 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