Table 2.
Proposed framework COVID-19 classification results on COVID-19 Radiography database.
| Classifiers | Features | Measures | |||||
|---|---|---|---|---|---|---|---|
| EffNet | VGG16 | SL EffNet | SL VGG16 | Proposed | Accuracy (%) | Time (%) | |
| Softmax | ✓ | 90.1 | 122.8954 | ||||
| ✓ | 90.6 | 151.4584 | |||||
| ✓ | 95.2 | 78.5363 | |||||
| ✓ | 95.0 | 91.6678 | |||||
| ✓ | 97.6 | 70.7674 | |||||
|
| |||||||
| Naïve Bayes | ✓ | 88.4 | 131.4453 | ||||
| ✓ | 88.9 | 162.5654 | |||||
| ✓ | 93.5 | 87.3422 | |||||
| ✓ | 93.8 | 97.0864 | |||||
| ✓ | 95.1 | 86.2355 | |||||
|
| |||||||
| MCSVM | ✓ | 91.0 | 126.4433 | ||||
| ✓ | 91.5 | 149.5465 | |||||
| ✓ | 93.9 | 81.6743 | |||||
| ✓ | 94.5 | 97.7682 | |||||
| ✓ | 95.9 | 76.3476 | |||||
|
| |||||||
| ELM | ✓ | 92.8 | 114.6752 | ||||
| ✓ | 88.5 | 136.8684 | |||||
| ✓ | 95.5 | 72.9005 | |||||
| ✓ | 95.2 | 86.0454 | |||||
| ✓ | 99.1 | 65.6294 | |||||