Table 3.
Comparative table for three kinds of DenseNet classifier models.
| SN | Attributes | DN-121 | DN-169 | DN-201 |
|---|---|---|---|---|
| 1 | # Layers | 430 | 598 | 710 |
| 2 | Learning Rate | 0.0001 | 0.0001 | 0.0001 |
| 3 | # Epochs | 20 | 20 | 20 |
| 4 | Loss | 0.003 | 0.0025 | 0.002 |
| 5 | ACC | 98 | 98.5 | 99 |
| 6 | SPE | 0.975 | 0.98 | 0.985 |
| 7 | F1-Score | 0.96 | 0.97 | 0.98 |
| 8 | Recall | 0.96 | 0.97 | 0.98 |
| 9 | Precision | 0.96 | 0.97 | 0.98 |
| 10 | AUC | 0.99 | 0.99 | 0.99 |
| 11 | Size (MB) | 93 | 165 | 233 |
| 12 | Batch size | 16 | 8 | 4 |
| 13 | Trainable Parameters | 80 M | 141 M | 200 M |
| 14 | Total Parameters | 81 M | 143 M | 203 M |
DN-121: DenseNet-121; DN-169: DenseNet-169; DN-201: DenseNet-201; # = number of. Bold highlights the superior performance of the DenseNet-201 (DN-201) model.