Performance evaluation of the ResNet50-CBAM
| Data split | Train test split | Five-fold cross-validation | |||||
|---|---|---|---|---|---|---|---|
| Model A | Model B | ||||||
| Metric | Model A (%) | Model B (%) | Average (%) | Mean (%) | Standard deviation | Mean (%) | Standard deviation |
| Accuracy | 99.43 | 98.50 | 98.97 | 99.35 | 0.009 | 98.68 | 0.014 |
| Recall | 99.01 | 96.11 | 97.56 | 98.54 | 0.022 | 96.49 | 0.021 |
| Precision | 98.7 | 97.74 | 98.22 | 98.90 | 0.014 | 97.68 | 0.017 |
| F1-Score | 99.0 | 96.91 | 97.96 | 98.70 | 0.018 | 97.04 | 0.014 |
| AUC | 99.25 | 97.63 | 98.44 | 99.06 | 0.013 | 97.75 | 0.015 |
| Data split | Train test split | Five-fold cross-validation | |||||
|---|---|---|---|---|---|---|---|
| Model A | Model B | ||||||
| Metric | Model A (%) | Model B (%) | Average (%) | Mean (%) | Standard deviation | Mean (%) | Standard deviation |
| Accuracy | 99.43 | 98.50 | 98.97 | 99.35 | 0.009 | 98.68 | 0.014 |
| Recall | 99.01 | 96.11 | 97.56 | 98.54 | 0.022 | 96.49 | 0.021 |
| Precision | 98.7 | 97.74 | 98.22 | 98.90 | 0.014 | 97.68 | 0.017 |
| F1-Score | 99.0 | 96.91 | 97.96 | 98.70 | 0.018 | 97.04 | 0.014 |
| AUC | 99.25 | 97.63 | 98.44 | 99.06 | 0.013 | 97.75 | 0.015 |
Source(s): Table created by the authors
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