Table 3.
Performance metrics of the machine learning model at different time intervals for the initial 10 images.
| Parameters | Accuracy | Recall | Precision | F1 score | MCC | Kappa | ROC | |
|---|---|---|---|---|---|---|---|---|
| Before cooling | Mean | 0.75 | 0.78 | 0.78 | 0.76 | 0.49 | 0.46 | 0.63 |
| SD | 0.07 | 0.26 | 0.03 | 0.15 | 0.15 | 0.14 | 0.06 | |
| 0 s | Mean | 0.71 | 0.73 | 0.78 | 0.73 | 0.39 | 0.37 | 0.66 |
| SD | 0.06 | 0.18 | 0.12 | 0.11 | 0.12 | 0.12 | 0.12 | |
| 15 s | Mean | 0.74 | 0.73 | 0.81 | 0.76 | 0.44 | 0.42 | 0.71 |
| SD | 0.07 | 0.13 | 0.12 | 0.08 | 0.21 | 0.20 | 0.13 | |
| 30 s | Mean | 0.61 | 0.58 | 0.83 | 0.60 | 0.39 | 0.31 | 0.57 |
| SD | 0.13 | 0.32 | 0.19 | 0.17 | 0.10 | 0.13 | 0.18 | |
| 45 s | Mean | 0.67 | 0.51 | 0.86 | 0.61 | 0.39 | 0.34 | 0.68 |
| SD | 0.09 | 0.23 | 0.15 | 0.20 | 0.18 | 0.17 | 0.14 | |
| 1 min | Mean | 0.71 | 0.96 | 0.68 | 0.79 | 0.42 | 0.33 | 0.58 |
| SD | 0.10 | 0.05 | 0.14 | 0.09 | 0.11 | 0.17 | 0.18 | |
| 1 min 15 s | Mean | 0.72 | 1.00 | 0.69 | 0.81 | 0.42 | 0.32 | 0.63 |
| SD | 0.13 | 0.00 | 0.13 | 0.09 | 0.19 | 0.23 | 0.18 | |
| 1 min 30 s | Mean | 0.73 | 0.83 | 0.75 | 0.77 | 0.44 | 0.41 | 0.60 |
| SD | 0.08 | 0.13 | 0.13 | 0.07 | 0.19 | 0.19 | 0.10 | |
| 1 min 45 s | Mean | 0.65 | 0.70 | 0.80 | 0.69 | 0.44 | 0.36 | 0.67 |
| SD | 0.12 | 0.28 | 0.20 | 0.12 | 0.12 | 0.18 | 0.17 | |
| 2 min | Mean | 0.67 | 0.64 | 0.86 | 0.68 | 0.43 | 0.35 | 0.64 |
| SD | 0.14 | 0.29 | 0.16 | 0.15 | 0.23 | 0.26 | 0.17 | |
| 2 min 15 sec | Mean | 0.67 | 0.74 | 0.80 | 0.70 | 0.42 | 0.32 | 0.66 |
| SD | 0.13 | 0.32 | 0.22 | 0.15 | 0.14 | 0.18 | 0.19 | |
Abbreviations: Kappa, Kappa hat coefficient; MCC, Matthews correlation coefficient; ROC, Receiver Operating Characteristic.