Artificial intelligence approaches on X-ray-oriented images process for early detection of COVID-19
Background: COVID-19 is a global public health problem that is crucially important to be diagnosed in the early stages. This study aimed to investigate the use of artificial intelligence (AI) to process X-ray-oriented images to diagnose COVID-19 disease. Methods: A systematic search was conducted in...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2022-01-01
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| Series: | Journal of Medical Signals and Sensors |
| Subjects: | |
| Online Access: | http://www.jmssjournal.net/article.asp?issn=2228-7477;year=2022;volume=12;issue=3;spage=233;epage=253;aulast=Rezayi |
| _version_ | 1818504407253254144 |
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| author | Sorayya Rezayi Marjan Ghazisaeedi Sharareh Rostam Niakan Kalhori Soheila Saeedi |
| author_facet | Sorayya Rezayi Marjan Ghazisaeedi Sharareh Rostam Niakan Kalhori Soheila Saeedi |
| author_sort | Sorayya Rezayi |
| collection | DOAJ |
| description | Background: COVID-19 is a global public health problem that is crucially important to be diagnosed in the early stages. This study aimed to investigate the use of artificial intelligence (AI) to process X-ray-oriented images to diagnose COVID-19 disease. Methods: A systematic search was conducted in Medline (through PubMed), Scopus, ISI Web of Science, Cochrane Library, and IEEE Xplore Digital Library to identify relevant studies published until 21 September 2020. Results: We identified 208 papers after duplicate removal and filtered them into 60 citations based on inclusion and exclusion criteria. Direct results sufficiently indicated a noticeable increase in the number of published papers in July-2020. The most widely used datasets were, respectively, GitHub repository, hospital-oriented datasets, and Kaggle repository. The Keras library, Tensorflow, and Python had been also widely employed in articles. X-ray images were applied more in the selected articles. The most considerable value of accuracy, sensitivity, specificity, and Area under the ROC Curve was reported for ResNet18 in reviewed techniques; all the mentioned indicators for this mentioned network were equal to one (100%). Conclusion: This review revealed that the application of AI can accelerate the process of diagnosing COVID-19, and these methods are effective for the identification of COVID-19 cases exploiting Chest X-ray images. |
| first_indexed | 2024-12-10T21:36:43Z |
| format | Article |
| id | doaj.art-1ff3deaee684460e91482346e339abb3 |
| institution | Directory Open Access Journal |
| issn | 2228-7477 |
| language | English |
| last_indexed | 2024-12-10T21:36:43Z |
| publishDate | 2022-01-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Journal of Medical Signals and Sensors |
| spelling | doaj.art-1ff3deaee684460e91482346e339abb32022-12-22T01:32:38ZengWolters Kluwer Medknow PublicationsJournal of Medical Signals and Sensors2228-74772022-01-0112323325310.4103/jmss.jmss_111_21Artificial intelligence approaches on X-ray-oriented images process for early detection of COVID-19Sorayya RezayiMarjan GhazisaeediSharareh Rostam Niakan KalhoriSoheila SaeediBackground: COVID-19 is a global public health problem that is crucially important to be diagnosed in the early stages. This study aimed to investigate the use of artificial intelligence (AI) to process X-ray-oriented images to diagnose COVID-19 disease. Methods: A systematic search was conducted in Medline (through PubMed), Scopus, ISI Web of Science, Cochrane Library, and IEEE Xplore Digital Library to identify relevant studies published until 21 September 2020. Results: We identified 208 papers after duplicate removal and filtered them into 60 citations based on inclusion and exclusion criteria. Direct results sufficiently indicated a noticeable increase in the number of published papers in July-2020. The most widely used datasets were, respectively, GitHub repository, hospital-oriented datasets, and Kaggle repository. The Keras library, Tensorflow, and Python had been also widely employed in articles. X-ray images were applied more in the selected articles. The most considerable value of accuracy, sensitivity, specificity, and Area under the ROC Curve was reported for ResNet18 in reviewed techniques; all the mentioned indicators for this mentioned network were equal to one (100%). Conclusion: This review revealed that the application of AI can accelerate the process of diagnosing COVID-19, and these methods are effective for the identification of COVID-19 cases exploiting Chest X-ray images.http://www.jmssjournal.net/article.asp?issn=2228-7477;year=2022;volume=12;issue=3;spage=233;epage=253;aulast=Rezayi2019-ncov diseaseartificial intelligencecomputed tomographydeep learningimage processingx-ray images |
| spellingShingle | Sorayya Rezayi Marjan Ghazisaeedi Sharareh Rostam Niakan Kalhori Soheila Saeedi Artificial intelligence approaches on X-ray-oriented images process for early detection of COVID-19 Journal of Medical Signals and Sensors 2019-ncov disease artificial intelligence computed tomography deep learning image processing x-ray images |
| title | Artificial intelligence approaches on X-ray-oriented images process for early detection of COVID-19 |
| title_full | Artificial intelligence approaches on X-ray-oriented images process for early detection of COVID-19 |
| title_fullStr | Artificial intelligence approaches on X-ray-oriented images process for early detection of COVID-19 |
| title_full_unstemmed | Artificial intelligence approaches on X-ray-oriented images process for early detection of COVID-19 |
| title_short | Artificial intelligence approaches on X-ray-oriented images process for early detection of COVID-19 |
| title_sort | artificial intelligence approaches on x ray oriented images process for early detection of covid 19 |
| topic | 2019-ncov disease artificial intelligence computed tomography deep learning image processing x-ray images |
| url | http://www.jmssjournal.net/article.asp?issn=2228-7477;year=2022;volume=12;issue=3;spage=233;epage=253;aulast=Rezayi |
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