Table 2.
Chest X-ray imaging tools, their datasets, and performance measured in Accuracy (ACC), Area Under the Curve (AUC), Specificity (SPEC), and Sensitivity (SEN)
| Authors (2020) | Dataset (size) | Performance (in %) | |||
|---|---|---|---|---|---|
| ACC | AUC | SPEC | SEN | ||
| Alqudah et al. [35] | Dataset: 71 images | 95.20 | – | 100.00 | 93.30 |
| Covid-19 + ve (48) + Covid-19 -ve (23) | |||||
| Ucar and Korkmaz [36] | Dataset (Kaggle): 5,310 images | 98.26 | – | 99.13 | – |
| Covid-19 (66) + normal (1,349) + pneumonia (3,895) | |||||
| Loey et al. [37] | Dataset: 307 images | 100.00 | – | – | 100.00 |
| Covid-19 + ve (69) + bacteria (79) + virus (79) + normal (79) | |||||
| Ozturk et al. [38] | Dataset: 1,127 images | 98.08 | – | 95.30 | 95.13 |
| Covid-19 + ve (127) + no-finding (500) + pneumonia (500) | |||||
| Mukherjee et al. [39] | Dataset (Kaggle): 260 images | 96.92 | 99.22 | 100.00 | 94.20 |
| Covid-19 + ve (130), Covid-19 -ve (130) | |||||
| Ozcan [40] | Dataset: 721 images | 97.69 | – | 97.90 | 97.26 |
| Covid-19 (131) + normal (200) + virus (148) + bacteria (242) | |||||
| Civit et al. [41] | Dataset: 396 images | 86.00 | 90.00 | 93.00 | 86.00 |
| Covid-19 (132) + pneumonia (132) + healthy (132) | |||||
| Rahimzadeh and Attar [42] | Dataset (RSNA): 15,085 images | 99.50 | – | 99.56 | 80.53 |
| Covid-19 (180) + pneumonia (6,054) + normal (8,851) | |||||
| Ismael and Şengür [43] (2021) | Dataset: 380 images | 94.74 | 99.90 | 98.89 | 91.00 |
| Covid-19 (180) + normal (200) | |||||
| Vaid et al. [44] | Dataset: 545 images | 96.33 | – | 97.05 | – |
| Covid-19 (181) + normal (364) | |||||
| Panwar et al. [45] | Dataset: 337 images | 97.62 | 88.09 | 78.57 | 97.62 |
| Covid-19 (192) + no-findings (145) | |||||
| Nour et al. [46] | Dataset: 2,905 images | 98.97 | 99.42 | 99.75 | 89.39 |
| Covid-19 (219) + pneumonia (1,345)+ normal (1,341) | |||||
| Apostolopoulos and Mpesiana [47] | Dataset: 1,442 images | 96.78 | – | 96.46 | 98.66 |
| Covid-19 (224) + pneumonia (714) + normal (504) | |||||
| Toraman et al. [48] | 2 Dataset: 2,331 images | 97.23 | – | 97.04 | 97.42 |
| Covid-19 (231) + others (1,050) + pneumonia (1,050) | |||||
| Brunese et al. [49] | Dataset: 6,523 images | 97.00 | – | 98.00 | 96.00 |
| Covid-19 (250) + pulmonary (2,753) + normal (3,520) | |||||
| Jain et al. [50] | Dataset: 1,215 images | 98.93 | – | 98.66 | 98.93 |
| Covid-19 + ve (250) + bacteria (300) + viral (350) + normal (315) | |||||
| Khan et al. [51] | Dataset (Kaggle): 1,251 images | 89.50 | – | – | 100.00 |
| Covid-19 (284) + bac (330) + viral (327) + normal (310) | |||||
| Sitaula and Hossain [52] | Dataset: 2,138 images | 87.49 | – | – | 96.00 |
| Covid (320) + Normal (500) + No findings (447) + pneumonia (871) | |||||
| Sitaula and Aryal [53] | Dataset: 2,138 images | 87.92 | – | – | – |
| Covid (320) + Normal (500) + No findings (447) + pneumonia (871) | |||||
| Wang et al. [54] | Dataset (RSNA): 13,972 images | 92.40 | – | – | 80.00 |
| Covid-19 + ve (358) + pneumonia (5,538) + normal (8,066) | |||||
| Ismael and Şengür [55] | Dataset: 561 images | 99.29 | – | 100.00 | 98.89 |
| Covid-19 (361) + normal (200) | |||||
| Marques et al. [56] | Dataset: 1,508 images | 99.63 | 97.00 | – | 99.63 |
| Covid-19 (504) + pneumonia (504) + normal (500) | |||||
| Das et al. [57] | Dataset: 18,524 images | 98.77 | 99.00 | 99.00 | 95.00 |
| Covid-19 (972) + pneumonia (9,560) + TB (400) + others (7,592) | |||||