Table 10 Experiments result of fine-tuned other parameters
From: Deep convolutional neural network based medical image classification for disease diagnosis
Configuration | VGG16 |
|---|---|
Model2 with last unfrozen ConvLayer, lr 0.0009 and lr decay 0.8 | 0.9 |
Model2 with last unfrozen ConvLayer, lr 0.001 and lr decay 0.5 | 0.873 |
Model2 with last unfrozen ConvLayer and 20,000 augmentation image | 0.871 |
Model2 with last unfrozen ConvLayer lr 0.0005, lr decay 0.5 and 10,000 augmentation image | 0.902 |
Model3 with last unfrozen ConvLayer drop rate 0.7 | 0.885 |
Model3 with drop rate 0.7 | 0.906 |
Model3 with drop rate 0.7 and 20,000 augmentation image | 0.922 |
Model3 with drop rate 0.7 and 30,000 augmentation image | 0.922 |
Model2 with last unfrozen ConvLayer, batch normal layer, drop rate 0.5 | 0.912 |
Model2 with last unfrozen ConvLayer, batch normal layer, drop rate 0.5 and 20,000 augmentation image | 0.906 |
Model2 with last unfrozen ConvLayer, batch normal layer, dropout 0.7, fc layer, dropout 0.5 | 0.916 |
Model2 with last unfrozen ConvLayer, batch normal layer, dropout 0.7, fc layer, dropout 0.5 and 20,000 augmentation image | 0.875 |