Table 2.
The performance evaluation of the proposed approach based on different classifiers and different DenseNet types in terms of accuracy (%) on the HeLa dataset.
| DenseNet type | Classifier | |||||
|---|---|---|---|---|---|---|
| 1-NN | 3-NN | 5-NN | MLP | Random forest | SVM | |
| 121 | 84.25 | 84.94 | 84.73 | 89.28 | 87.54 | 90.18 |
| 169 | 85.28 | 86.73 | 86.08 | 93.36 | 92.49 | 92.75 |
| 201 | 85.17 | 86.06 | 85.97 | 91.85 | 90.92 | 91.18 |
| 264 | 84.39 | 85.12 | 84.67 | 89.47 | 88.28 | 90.07 |