Table 4.
Performance comparison of recent prediction models on various Breast Cancer datasets. Classifiers with ’*’ represent the classifiers with the highest ACC scores in the respective paper
| Work Ref. | Classifier | Performance metrics | |||||
|---|---|---|---|---|---|---|---|
| ACC | P | R | F1 | S | AUC | ||
| BreakHis Dataset | |||||||
| Han et al. [79] | CSDCNN | 0.93 | – | – | – | – | – |
| Sudharshan et al. [212] | MILCNN* | 0.92 | – | – | – | – | – |
| Shallu and Mehra [199] | VGG 16 + LR | 0.92 | 0.93 | 0.93 | 0.93 | – | 0.95 |
| Jannesari et al. [105] | ResNet V1 152 | 0.98 | 0.99 | – | – | – | 0.98 |
| Gupta and Chawla [77] | ResNet50+LR | 0.93 | – | – | – | – | – |
| Chattopadhyay et al. [38] | DRDA-Net | 0.98 | – | – | – | – | – |
| BACH Dataset | |||||||
| Rakhlin et al. [172] | LightGBM + CNN | 0.87 | – | – | – | – | – |
| Yang et al. [230] | EMS-Net | 0.91 | – | – | – | – | – |
| Roy et al. [178] | Self-designed (OPOD) | 0.77 | 0.77 | – | 0.77 | 0.77 | – |
| Roy et al. [178] | Self-designed (APOD) | 0.90 | 0.92 | – | 0.90 | 0.90 | – |
| Sanyal et al. [189] | Hybrid Ensemble (OPOD) | 0.87 | 0.86 | 0.87 | 0.86 | 0.99 | – |
| Sanyal et al. [189] | Hybrid Ensemble (APOD) | 0.95 | 0.95 | 0.95 | 0.95 | 0.98 | – |
| Bhowal et al. [28] | Choquet fuzzy integral and coalition game based classifier ensemble | 0.95 | – | – | – | – | – |
| Mics. Dataset | |||||||
| Yan et al. [228] | Inception-V3* | 0.91 | – | 0.87 | – | – | 0.89 |
| Dey et al. [52] | DenseNet-121 | 0.99 | 0.99 | 0.98 | 0.98 | – | – |