# Performance of Fine-Tuning Convolutional Neural Networks for HEp-2 Image Classification
## 4 Results
In the case of non-separable data, a hyperplane is sought that separates many, but not necessarily all the data, through the formulation of a \"soft margin\" using the penalty parameter C (or error term). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**CNN** & **AUC** & **Learning Rate** & **Mini-batch** & **Epoch** \\ \hline AlexNet & 98.08\% & 0.001 & 16 & 4 \\ \hline SqueezeNet & 98.63\% & 0.001 & 16 & 6 \\ \hline ResNet18 & 98.33\% & 0.001 & 16 & 8 \\ \hline GoogLeNet & 98.37\% & 0.01 & 32 & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Best Hyperparameter. Figure 5: ROC (Receiver Operating Characteristic) curve of the best configuration. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**CNN Name** & \begin{tabular}{c} **High** \\ **Frozen** \\ \end{tabular} & \begin{tabular}{c} **Medium** \\ **Frozen** \\ \end{tabular} & \begin{tabular}{c} **Low** \\ **Frozen** \\ \end{tabular} & \
\begin{tabular}{c} **Scratch** \\ **Augmentation** \\ \end{tabular} &
\begin{tabular}{c} **Low Frozen + Data** \\ **Augmentation** \\ \end{tabular} \\ \hline AlexNet & 97.20\% & 97.93\% & 97.82\% & 98.00\% & 98.08\% & 98.02\% \\ \hline SqueezeNet & 97.96\% & 98.39\% & 98.55\% & 98.38\% & 98.63\% & 98.46\% \\ \hline ResNet18 & 97.27\% & 97.88\% & 98.11\% & 98.24\% & 98.33\% & 98.32\% \\ \hline GoogLeNet & 96.30\% & 96.78\% & 98.00\% & 97.96\% & 98.37\% & 98.20\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Best AUC results. The linear SVM training phase was carried out by extracting the features, for the various layers, from all the training images. The SVM parameter C has been optimized on the validation set. The validated model was subsequently used for the test. Table 7 shows the best results obtained, in terms of AUC, for each pre-trained network analyzed and reports the layers that provided the best results. The best result obtained with the CNNs used as a features extractor coupled to the SVM classifier reached an AUC equal to 97.8%. This result was obtained with the ResNet18 CNN. The SVM parameter C associated with the best configuration was C = 0.0298. The results in Table 7, when compared with those of Table 5, show that, with the same CNN used, the fine-tuning method achieves better performances than the other method. Moreover, the results obtained in this work were compared with other state-of-the-art works in Table 8.  
Unfortunately, not all of the authors of these works in Table 8 calculated the AUC value. While, from the comparison in accuracy it is verified that our method turns out to be the second best. However, it should be noted that while [PERSON] et al. [34] aimed to maximize accuracy, the AUC was maximized in this work. For a more direct comparison with other methods that based their optimization on accuracy, we repeated the analysis using the latter index as a figure of merit, obtaining for the best configuration (also, this time with the SqueezeNet network), the result of 94.32% accuracy and an AUC equal to 98.34%. It should also be noted that the AIDA database used in this work (as reported in sub-Section 3.1) is varied, both in terms of patterns contained (more than 20, both in single and multiple forms) and in terms of instruments and methods of acquisition (manufacturers of kits and instruments employed were different site-to-site); this makes it particularly difficult to classify.