Table 7.
Performance comparison with other BCC classification methods
| Manuscript | Dataset | Feature categories | Final classifier | Accuracy | Sensitivity | Precision |
|---|---|---|---|---|---|---|
| Kharazmi et al. 2017 | 659; 299 BCC and 360 non-BCC | Vascular features | Random Forest | 0.965 | 0.904 | 0.952 |
| Kharazmi et al. 2018 | 1199; 599 BCC and 600 non-BCC | Patient profile information & SAE feature learning | Softmax | 0.911 | 0.853 | 0.877 |
| Serrano et al. 2022 | 692 BCC and 671 non-BCC | Color and texture features | MLP | 0.970 | 0.993 | 0.953 |
| Proposed method | 2000; 1000 BCC and 1000 non-BCC | EfficientNet-B5 & localized vessel handcrafted color and shape features | Random Forest | 0.972 | 0.979 | 0.965 |