Application of Machine Learning in Diagnosis of COVID-19 Through X-Ray and CT Images: A Scoping Review

被引：50

作者：

Mohammad-Rahimi, Hossein ^{[1
]}

Nadimi, Mohadeseh ^{[2
,3
]}

Ghalyanchi-Langeroudi, Azadeh ^{[2
,3
]}

Taheri, Mohammad ^{[4
]}

Ghafouri-Fard, Soudeh ^{[5
]}

机构：

[1] Shahid Beheshti Univ Med Sci, Res Inst Dent Sci, Dent Res Ctr, Tehran, Iran

[2] Univ Tehran Med Sci, Dept Med Phys & Biomed Engn, Tehran, Iran

[3] Res Ctr Biomed Technol & Robot RCBTR, Tehran, Iran

[4] Shahid Beheshti Univ Med Sci, Urol & Nephrol Res Ctr, Tehran, Iran

[5] Shahid Beheshti Univ Med Sci, Dept Med Genet, Tehran, Iran

来源：

FRONTIERS IN CARDIOVASCULAR MEDICINE | 2021年 / 8卷

关键词：

COVID-19; machine learning; detection; biomarker; X-ray image; CONVOLUTIONAL NEURAL-NETWORK; DISEASE; 2019; COVID-19; CORONAVIRUS DISEASE; CHEST CT; DEEP; PNEUMONIA; CLASSIFICATION; SEVERITY; MODEL; GAN;

D O I：

10.3389/fcvm.2021.638011

中图分类号：

R5 [内科学];

学科分类号：

1002 ; 100201 ;

摘要：

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.

引用

页数：25

共 116 条

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