Selected article for: "chest CT diagnosis and diagnosis performance"
Author: Mohammad-Rahimi, Hossein; Nadimi, Mohadeseh; Ghalyanchi-Langeroudi, Azadeh; Taheri, Mohammad; Ghafouri-Fard, Soudeh
Title: Application of Machine Learning in Diagnosis of COVID-19 Through X-Ray and CT Images: A Scoping Review
  • Cord-id: 8tag0v55
  • Document date: 2021_3_25
    • ID: 8tag0v55
    Snippet: 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. I
    Document: 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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