Table 6.
Results of the application of convolutional neural networks in other tumor fields.
| Pathological Type | Authors | Model | Results | |
|---|---|---|---|---|
| Accuracy | Sensitivity | |||
| Osteoma | Barzekar and Yu [119] | C-Net | 99.34% | - |
| Mishra et al. [120] | CNN | 92.4% | - | |
| Breast tumors | Singh et al. [121] | cGAN-CNN | 80% | - |
| Ting et al. [122] | CNNI-BCC | 90.5% | - | |
| Bakkouri and Afdel [123] | CNN-softmax | 97.28% | - | |
| Wang et al. [124] | ABVS-CADe | - | 100% | |
| Toğaçar et al. [125] | BreastNet | 98.8% | - | |
| Zhang et al. [126] | BDR-CNN-GCN | 96.1% | 96.2% | |
| Zeimarani et al. [127] | CNN-US | 92.01% | - | |
| Alom et al. [128] | IRRCNN (binary) | 99.05% | - | |
| IRRCNN (multi-class) |
98.59% | - | ||
| Digestive tract tumors | Hirasawa et al. [129] | SSD | - | 92.2% |
| Li et al. [5] | CNN-M-NBI | 90.91% | - | |