# Performance of Fine-Tuning Convolutional Neural Networks for HEp-2 Image Classification
## 5 Discussions and Conclusions  
In this work, the discriminating capabilities of pre-trained CNNs applied to the problem of the classification of fluorescence intensity in HEp-2 images were investigated, a problem of crucial importance for the diagnosis of autoimmune diseases. To this end, four of the best known CNNs have been analyzed with the fine-tuning training method. In particular, the effectiveness of the fine-tuning method was verified in different freezing levels, in training from scratch mode and with and without data augmentation. The method was developed and tested using the AIDA public database. The best explored configuration showed in terms of performance: AUC of 98.6% and an accuracy of 93.9%.  
The best configuration analyzed was compared with other state-of-the-art methods demonstrating an excellent ability to classify the fluorescence intensity in HEp-2 images. Unfortunately, in [34] a private database was used, so a more direct comparison on the same data is not possible. The difference in accuracy between our method and that of [PERSON] et al., even after optimization in accuracy, is still significant. Perhaps the best performance obtained in [34] is not due to a lower capacity of the CNN fine-tuning, but rather (in addition to the different database used) to the fact that, in [34], a segmentation of the images was performed in search of the cells, and starting from these, a clustering was performed that led to image classification. In this way, the method, effectively eliminating the background, shows greater robustness and, therefore, better performance.  
The effectiveness of the fine-tuning technique was verified by comparing the performance of the same CNNs used by feature extractors (and coupled to SVM-type classifiers). With the same CNN used, fine-tuning has always given better results than the other method. It was possible to verify, for all the pre-trained networks analyzed, an, albeit, slight increase in performance with the use of the data augmentation method. Furthermore, as expected, for each network, the best performances were obtained with low freezing of the layers. The best performing network for the classification of fluorescence intensity in IIF images was SqueezeNet.  
It should be noted that the CNNs tested in the various configurations obtained performances all contained in a very small range (about 3% of AUC).This denotes very similar classifying abilities (at least of the specific problem) of the CNN analyzed.  
[PERSON] developed the software, optimized the parameters, and helped to write the draft. [PERSON] conceived of the study, performed the statistical analysis, and drafted the manuscript. [PERSON] assisted in the statistical analysis and writing of the manuscript. [PERSON] participated in the design and coordination of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**CNN Name**} & \multicolumn{4}{c}{**Training Time in Hours (min–max)**} \\ \cline{2-5}  & **High Frozen** & **Medium Frozen** & **Low Frozen** & **Scratch** \\ \hline AlexNet & (3.42–9.57) & (4.33–12.58) & (4.69–17.24) & (5.65–26.3) \\ \hline SqueezeNet & (6.26–10.22) & (7.43–14.15) & (11.93–27.96) & (12.56–36.57) \\ \hline ResNet18 & (4.28–9.94) & (4.51–10.51) & (4.91–11.52) & (5.86–18.12) \\ \hline GoogLeNet & (4.02–8.26) & (4.13–16.32) & (4.27–18.54) & (5.37–24.4) \\ \hline \hline \end{tabular}
\end{table}
Table 9: CNNs training times.