Table 7.
Comparison with previous approaches using pretrained architectures for classification of the ICIAR2018 dataset.
Reference | Architecture | Stain normalization | Accuracy |
---|---|---|---|
Nawaz et al. [3] | AlexNet | Macenko | 81.25% |
Ferreria et al. [20] | Inception-ResNet-v2 | None | 90% |
Kassani et al. [21] | VGG16 | Macenko | 83% |
Reinhard | 87% | ||
Kassani et al. [21] | VGG19 | Macenko | 80% |
Reinhard | 84% | ||
Kassani et al. [21] | Inception-ResNet-v2 | Macenko | 90% |
Reinhard | 88% | ||
Kassani et al. [21] | Xception | Macenko | 91% |
Reinhard | 94% | ||
Kassani et al. [21] | Inception-v3 | Macenko | 90% |
Reinhard | 90% | ||
Golatkar et al. [50] | Inception-v3 | Vahadane | 85% |
Vesal et al. [51] | Inception-v3 | Reinhard | 97.08% |
Vesal et al. [51] | ResNet-50 | Reinhard | 96.66% |
Our approach | EfficientNet-B2 | Reinhard | 98.33% |
Our approach | EfficientNet-B2 | Macenko | 96.67% |