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10.3389/fcvm.2021.638011

http://scihub22266oqcxt.onion/10.3389/fcvm.2021.638011
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33842563!8027078!33842563
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suck abstract from ncbi


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pmid33842563      Front+Cardiovasc+Med 2021 ; 8 (ä): 638011
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  • Application of Machine Learning in Diagnosis of COVID-19 Through X-Ray and CT Images: A Scoping Review #MMPMID33842563
  • Mohammad-Rahimi H; Nadimi M; Ghalyanchi-Langeroudi A; Taheri M; Ghafouri-Fard S
  • Front Cardiovasc Med 2021[]; 8 (ä): 638011 PMID33842563show ga
  • Coronavirus disease, first detected in late 2019 (COVID-19), has spread fast throughout the world, leading to high mortality. This condition can be diagnosed using RT-PCR technique on nasopharyngeal and throat swabs with sensitivity values ranging from 30 to 70%. However, chest CT scans and X-ray images have been reported to have sensitivity values of 98 and 69%, respectively. The application of machine learning methods on CT and X-ray images has facilitated the accurate diagnosis of COVID-19. In this study, we reviewed studies which used machine and deep learning methods on chest X-ray images and CT scans for COVID-19 diagnosis and compared their performance. The accuracy of these methods ranged from 76% to more than 99%, indicating the applicability of machine and deep learning methods in the clinical diagnosis of COVID-19.
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