TABLE V. Accuracy of COVID-19 Classification Network With Different Training Strategies. (PRE.–Precision, REC.–Recall (%)).
| Number of Negative Samples | |||||||||
| 14% (246) | 28% (500) | 56% (1,000) | |||||||
| PRE. | REC. | F1 | PRE. | REC. | F1 | PRE. | REC. | F1 | |
| Random | 81.0 | 68.0 | 73.9 | 68.8 | 88.0 | 77.2 | 82.0 | 82.0 | 82.0 |
| Normal distribution | 77.6 | 76.0 | 76.8 | 70.8 | 92.0 | 80.0 | 85.7 | 84.0 | 84.8 |
Top-
|
41.8 | 66.0 | 51.2 | 60.0 | 78.0 | 67.8 | 84.6 | 88.0 | 86.3 |
| Long-tail distribution | 74.5 | 82.0 | 78.1 | 74.2 | 92.0 | 82.1 | 93.0 | 80.0 | 86.0 |
| Long-tail w/ Rubik's cube Pro* | 75.9 | 88.0 | 81.5 | 78.9 | 90.0 | 84.1 | 97.7 | 84.0 | 90.3 |
| * Rubik's cube Pro trained with all samples. | |||||||||