| Ali (2020) [50] |
CXR |
https://www.kaggle.com/ahmedali2019/pneumonia-sample-xrays |
[51] |
| BIMCV (2020) [52] |
CXR |
https://bimcv.cipf.es/bimcv-projects/padchest/ |
[53] |
| CC-CCII database [54] |
CT |
http://ncov-ai.big.ac.cn/download?lang = en |
[30,55] |
| Chest Imaging (2020) [56] |
CXR |
https://threadreaderapp.com/thread/1243928581983670272.html |
[5,57] |
| Chung (2020) [58] |
CXR |
https://github.com/agchung/Actualmed-COVID-chestxray-dataset |
[26,57,[59], [60], [61]] |
| Cohen et al. (2020) [62] |
CXR and CT |
https://github.com/ieee8023/covid-chestxray-dataset |
[5,9,10,23,[26], [27], [28],51,53,55,57,[59], [60], [61],[63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77], [78], [79], [80], [81], [82], [83], [84], [85], [86], [87], [88], [89], [90]] |
| COVIDGR [91] |
CXR |
https://dasci.es/es/transferencia/open-data/covidgr/ |
[91] |
| Dadario AMV. COVID-19 X-rays |
CXR and CT |
http://dx.doi.org/10.34740/KAGGLE/DSV/1019469 |
[72] |
| European Society of Radiology [92] |
CXR and CT |
https://www.eurorad.org/advanced-search?search=COVID |
[65] |
| Gunraj et al. (2020) [93] |
CT |
https://www.kaggle.com/hgunraj/covidxct?select=2A_images |
[94] |
| Irvin et al. (2019) [95] |
CXR |
https://stanfordmlgroup.github.io/competitions/chexpert/ |
[57] |
| Jaeger et al. [96] |
CXR |
https://openi.nlm.nih.gov/faq#faq-tb-coll |
[23,27] |
| JSRT [97] |
CXR |
http://db.jsrt.or.jp/eng-01.php |
[23,27,70] |
| Kermany et al. (2018) [48] |
CXR |
https://data.mendeley.com/datasets/rscbjbr9sj/2 |
[23,69,72,75,77,81,89,90,98,99] |
| Khoong (2020) [100] |
CXR |
https://www.kaggle.com/khoongweihao/covid19-xray-dataset-train-test-sets |
[59] |
| LIDC–IDRI database [101] |
CT |
https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI |
[30] |
| Montgomery tuberculosis [96] |
CXR |
https://www.kaggle.com/raddar/tuberculosis-chest-xrays-montgomery |
[23,27] |
| Mooney (2017) [49] |
CXR |
https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia/version/2 |
[5,10,26,57,60,[63], [64], [65],67,72,73,76,85,87,88] |
| MosMedData [102] |
CT |
https://mosmed.ai/datasets/covid19_1110/ |
[30,103] |
| Patel et al. (2020) [104] |
CXR |
https://www.kaggle.com/prashant268/chest-xray-covid19-pneumonia |
[94] |
| Praveen et al. (2020) [105] |
CXR |
https://www.kaggle.com/praveengovi/coronahack-chest-xraydataset |
[27] |
| Rahman et al. (2020) [106] |
CXR |
https://www.kaggle.com/tawsifurrahman/covid19-radiography-database |
[5,51,59,61,74,75,107] |
| Radiology Assistant |
CXR and CT |
https://radiologyassistant.nl/chest/covid-19/covid19-imaging-findings |
[63] |
| Radiopaedia [108] |
CXR and CT |
https://radiopaedia.org/search?lang = us&q = covid&scope = cases |
[5,9,26,60,79,90,109,110] |
| RSNA (2020) [111] |
CXR |
https://www.kaggle.com/c/rsna-pneumonia-detection-challenge |
[5,26,28,80,90,109,112] |
| Sajid [113] |
CXR |
https://www.kaggle.com/nabeelsajid917/covid-19-x-ray-10000-images |
[59] |
| Shenzhen [114] |
CXR |
https://lhncbc.nlm.nih.gov/LHC-publications/pubs/TuberculosisChestXrayImageDataSets.html |
[23] |
| SIRM (2020) [115] |
CXR and CT |
https://sirm.org/category/senza-categoria/covid-19/ |
[5,26,57,60,65,90,109,110] |
| SARS-COV-2 CT-Scan (2020) [116] |
CT |
https://www.kaggle.com/plameneduardo/sarscov2-ctscan-dataset |
[9,59,117,118] |
| Tianchi-Alibaba database [119] |
CT |
https://tianchi.aliyun.com/dataset/dataDetail?dataId = 90014 |
[30] |
| USCD-AI4H [120] |
CT |
https://github.com/UCSD-AI4H/COVID-CT |
[10,59,117,118,[121], [122], [123]] |
| Vaya et al. (2020) [124] |
CXR and CT |
https://bimcv.cipf.es/bimcv-projects/bimcv-covid19/ |
[23,53] |
| Wang et al. (2017) [125] |
CXR |
https://github.com/muhammedtalo/COVID-19/tree/master/X-Ray, https://www.kaggle.com/nih-chest-xrays/sample
|
[53,66,68,69,79,83,84,98] |
| Wang et al. (2020) [126] |
CXR |
https://github.com/lindawangg/COVID-Net |
[112] |
| Yan et al. (2020) [127] |
CT |
https://ieee-dataport.org/authors/tao-yan |
[103] |
