Table III.
Type % | ACC | SEN | SPC | AUC |
---|---|---|---|---|
Binary | ||||
Proposed | 100±1.0 | 99±2.0 | 100±0.0 | 100±0.0 |
Zhang et al (14) | - | up to 96 | 70.6 | 95.1 |
Narin et al (15) | 98.0 | 96.0 | 100.0 | - |
Afshar et al (17) | 98.3 | 80.0 | 98.6 | - |
Khalifa et al (19) | 98.7 | 98.7 | 98.7 | - |
Apostolopoulos et al (22) | 96.7 | 98.6 | 96.46 | - |
Chowdhury et al (37) | 98.3 | 96.7 | 100.0 | 99.8 |
Ternary | ||||
Proposed | 85±7.0 | 94±6 | 92.7±7.6 | 96±2.0 |
Wang et al (16) | 92.6 | 91.3 | - | - |
Abbas et al (18) | 95.1 | 97.9 | 91.8 | - |
Ucar et al (21) | 98.2 | - | 99.1 | - |
Apostolopoulos et al (22) | 94.7 | - | - | - |
Chowdhury et al (37) | 98.3 | 96.7 | 99.0 | 99.0 |
Quaternary | ||||
Proposed | 76±8.0 | 93±9 | 91.8±7.6 | 93±3.0 |
aThe metrics are presented in mean ± standard deviation format, regarding the COVID-19 class for each case. The best performance is presented in bold. ACC, accuracy; SEN, sensitivity; SPC, specificity; AUC, area under the curve.