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. 2020 Jan 23;10:1012. doi: 10.1038/s41598-020-57875-1

Table 3.

The average test classification accuracy.

DCNN model Augmentation Fine-tuning LR Accuracy (%)
VGG-16 5e-6 38.96
5e-6 56.76
5e-6 91.15
5e-6 97.19
ResNet-50 5e-3 57.47
1e-3 57.74
5e-3 93.45
7.5e-3 96.86
SqueezeNet 3e-5 47.42
3e-5 62.81
3e-5 78.58
3e-5 90.71

Each model is trained with four settings of dynamic data-augmentation and fine-tuning. When a model is not fine-tuned, it is trained from scratch with random initialization.

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