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- Title
Artificial intelligence approaches on X-ray-oriented images process for early detection of COVID-19.
- Authors
Rezayi, Sorayya; Ghazisaeedi, Marjan; Kalhori, Sharareh; Saeedi, Soheila
- Abstract
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.
- Subjects
IMAGE processing; ARTIFICIAL intelligence; COVID-19; X-ray imaging; COVID-19 pandemic; PYTHON programming language
- Publication
Journal of Medical Signals & Sensors, 2022, Vol 12, Issue 3, p233
- ISSN
2228-7477
- Publication type
Article
- DOI
10.4103/jmss.jmss_111_21