# How Deeply to Fine-Tune a Convolutional Neural Network: A Case Study Using a Histopathology Dataset
## 4 Results
### Experiment Results on a Different Histopathology Dataset
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**VGG16 Test AUC Results**} \\ \hline
**Blocks** & **LR=\(10^{-3}\)** & **LR=\(10^{-4}\)** & **LR=\(10^{-5}\)** \\ \hline Fine-Tuning 5 th Block & 89.51\% & 91.76\% & 89.73\% \\ Fine-Tuning 4 th Block & 88.50\% & 93.58\% & 94.03\% \\ Fine-Tuning 3 rd Block & 86.21\% & 95.39\% & 95.76\% \\ Fine-Tuning 2 nd Block & 86.79\% & 94.90\% & 93.91\% \\ Fine-Tuning ALL & 85.04\% & 92.47\% & 93.04\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of the VGG16 architecture using the BreakHis dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**VGG19 Test AUC Results**} \\ \hline
**Blocks** & **LR=\(10^{-3}\)** & **LR=\(10^{-4}\)** & **LR=\(10^{-5}\)** \\ \hline Fine-Tuning 5 th Block & 89.14\% & 90.11\% & 88.68\% \\ Fine-Tuning 4 th Block & 87.41\% & 91.67\% & 93.80\% \\ Fine-Tuning 3 rd Block & 88.72\% & 96.79\% & 94.46\% \\ Fine-Tuning 2 nd Block & 50.00\% & 95.46\% & 94.39\% \\ Fine-Tuning ALL & 87.29\% & 95.26\% & 94.30\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of the VGG19 architecture using the BreakHis dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**InceptionV3 Test AUC Results**} \\ \hline
**Blocks** & **LR=\(10^{-3}\)** & **LR=\(10^{-4}\)** & **LR=\(10^{-5}\)** \\ \hline Fine-Tuning 13 th Block & 55.09\% & 55.80\% & 55.26\% \\ Fine-Tuning 12 th Block & 61.61\% & 57.78\% & 53.45\% \\ Fine-Tuning 11 th Block & 56.92\% & 56.74\% & 62.26\% \\ Fine-Tuning 10 th Block & 61.58\% & 54.85\% & 53.54\% \\ Fine-Tuning 9 th Block & 59.33\% & 55.35\% & 51.61\% \\ Fine-Tuning 8 th Block & 51.26\% & 50.79\% & 50.63\% \\ Fine-Tuning 7 th Block & 58.38\% & 51.62\% & 50.41\% \\ Fine-Tuning 6 th Block & 56.63\% & 50.90\% & 53.38\% \\ Fine-Tuning 5 th Block & 56.87\% & 50.52\% & 50.64\% \\ Fine-Tuning 4 th Block & 57.46\% & 54.39\% & 50.57\% \\ Fine-Tuning 3 rd Block & 52.00\% & 50.00\% & 50.00\% \\ Fine-Tuning 2 nd Block & 50.00\% & 50.00\% & 52.00\% \\ Fine-Tuning ALL & 85.69\% & 94.72\% & 94.41\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results of the InceptionV3 architecture using the BreakHis dataset. In particular, with this architecture, fine-tuning the top layers did not achieve any satisfactory results. As for the PatchCamelyon dataset, the best results were achieved by fine-tuning the entire network. Finally, Table 9 summarizes the results obtained by training the architectures from scratch on the BreakHis dataset. According to the results reported in Table 9, when considering the VGG16 architecture and the highest learning rate, the model did not converge at all for the stopping criteria we imposed. By using the medium learning rate, the model converged to an acceptable performance, which is the highest in this set of experiments but was lower than the result obtained by fine-tuning the network, it showed the lowest learning rate and performs poorer with respect to the network trained by considering the medium learning rate value. Focusing on the VGG19 architecture, it did behave the same as the VGG16 for all the considered learning rates. More in detail, the best performance was obtained with a learning rate of \(10^{-4}\). Considering the InceptionV3 architecture, the results follow the same trend observed with the PatchCamelyon dataset. In particular, the training process did converge for the smaller learning rate, and the best performance was obtained by using the medium learning rate value. Similar to the results achieved on the PatchCamelyon dataset, Table 9 shows that training the network from scratch did not achieve better results than fine-tuning the network.