# 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  
To corroborate the results obtained with the PatchCamelyon dataset and to strengthen our findings, we performed a second set of experiments using a different histopathology dataset, namely the BreakHis [18] dataset. The BreakHis dataset consists of 7909 images split into 2480 benign images and 5429 malignant images. To conduct our experiments, we split the dataset into 80% to train the model, 10% to validate the model during training, and 10% to test the model on unseen data. The early stopping was increased to 50 epochs because of the dataset size. The results of fine-tuning the BreakHis dataset using the VGG16 architecture are shown in Table 6, the results of using VGG19 are summarized in Table 7, and lastly, the results of using InceptionV3 are shown in Table 8. \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 & 0.8250 & 0.8220 & 0.8221 \\ Fine-Tuning 12 th Block & 0.8514 & 0.8466 & 0.8446 \\ Fine-Tuning 11 th Block & 0.8702 & 0.8446 & 0.8274 \\ Fine-Tuning 10 th Block & 0.8648 & 0.8675 & 0.8429 \\ Fine-Tuning 9 th Block & 0.8526 & 0.8816 & 0.8538 \\ Fine-Tuning 8 th Block & 0.8574 & 0.8637 & 0.8469 \\ Fine-Tuning 7 th Block & 0.8673 & 0.8429 & 0.8907 \\ Fine-Tuning 6 th Block & 0.8923 & 0.8468 & 0.8950 \\ Fine-Tuning 5 th Block & 0.8680 & 0.8730 & 0.8883 \\ Fine-Tuning 4 th Block & 0.8483 & 0.8335 & 0.8686 \\ Fine-Tuning 3 rd Block & 0.7715 & 0.8575 & 0.8641 \\ Fine-Tuning 2 nd Block & 0.7175 & 0.8636 & 0.8785 \\ Fine-Tuning ALL & 0.8071 & 0.9058 & 0.9280 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the InceptionV3 architecture. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Training from Scratch AUC Results**} \\ \hline
**Networks** & **LR=10\({}^{-3}\)** & **LR=10\({}^{-4}\)** & **LR=10\({}^{-5}\)** \\ \hline VGG16 & 50\% & 90.55\% & 85.91\% \\ VGG19 & 50\% & 84.77\% & 85.81\% \\ InceptionV3 & 84.83\% & 87.93\% & 81.97\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of different architectures trained from scratch. As one can see from the analysis of Table 6, the results obtained by fine-tuning VGG16 using the BreakHis dataset match the results obtained on the PatchCamelyon dataset. In particular, fine-tuning the top layers and using the smallest learning rates of \(10^{-4}\) and \(10^{-5}\) did achieve the highest results. More in detail, with a learning rate of \(10^{-3}\), the best performance was achieved by fine-tuning only the 5 th block; with a learning rate of \(10^{-4}\), the best performance was obtained through the fine-tuning of blocks from the 5 th to the 3 rd. Finally, the overall best performance was obtained with a learning rate of \(10^{-5}\) and with the fine-tuning of blocks from the 5 th to the 3 rd. Also, for the results obtained by fine-tuning VGG19 using the BreakHis dataset (Table 7), one can notice a similar behavior with respect to the analysis performed with the PatchCamelyon dataset: fine-tuning the top layers was sufficient to produce the best results, and the smaller learning rates yielded the best performance. More in detail, the best performance when considering a learning rate of \(10^{-3}\) was obtained by only fine-tuning the 5 th block. On the other hand, for both the remaining learning rates of \(10^{-4}\) and \(10^{-5}\), the best performance was achieved by fine-tuning blocks from the 5 th to the 3 rd. Moving to the results obtained by fine-tuning InceptionV3 using the BreakHis dataset (Table 8), it is possible to observe the same pattern already discussed when we considered the PatchCamelyon dataset.