{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:08:32Z","timestamp":1772906912241,"version":"3.50.1"},"reference-count":82,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T00:00:00Z","timestamp":1671667200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["International Journal of Biomedical Imaging"],"published-print":{"date-parts":[[2022,12,22]]},"abstract":"<jats:p>This paper presents an automated and noninvasive technique to discriminate COVID-19 patients from pneumonia patients using chest X-ray images and artificial intelligence. The reverse transcription-polymerase chain reaction (RT-PCR) test is commonly administered to detect COVID-19. However, the RT-PCR test necessitates person-to-person contact to administer, requires variable time to produce results, and is expensive. Moreover, this test is still unreachable to the significant global population. The chest X-ray images can play an important role here as the X-ray machines are commonly available at any healthcare facility. However, the chest X-ray images of COVID-19 and viral pneumonia patients are very similar and often lead to misdiagnosis subjectively. This investigation has employed two algorithms to solve this problem objectively. One algorithm uses lower-dimension encoded features extracted from the X-ray images and applies them to the machine learning algorithms for final classification. The other algorithm relies on the inbuilt feature extractor network to extract features from the X-ray images and classifies them with a pretrained deep neural network VGG16. The simulation results show that the proposed two algorithms can extricate COVID-19 patients from pneumonia with the best accuracy of 100% and 98.1%, employing VGG16 and the machine learning algorithm, respectively. The performances of these two algorithms have also been collated with those of other existing state-of-the-art methods.<\/jats:p>","DOI":"10.1155\/2022\/5318447","type":"journal-article","created":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T14:50:08Z","timestamp":1671720608000},"page":"1-15","source":"Crossref","is-referenced-by-count":8,"title":["Chest X-Ray Images to Differentiate COVID-19 from Pneumonia with Artificial Intelligence Techniques"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4713-6821","authenticated-orcid":true,"given":"Rumana","family":"Islam","sequence":"first","affiliation":[{"name":"Department of ECE, University of Windsor, ON, N9B 3P4, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9914-0147","authenticated-orcid":true,"given":"Mohammed","family":"Tarique","sequence":"additional","affiliation":[{"name":"Department of ECE, University of Science and Technology of Fujairah, UAE"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.23750\/abm.v91i1.9397"},{"key":"2","article-title":"Worldometer Corona Virus Cases"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-60188-1_2"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-16-6285-0_54"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1021\/acsnano.0c02624"},{"key":"6","article-title":"Half of world population lacks access to essential health services- are we doing enough","volume-title":"World Economic Forum","author":"K. 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