# Performance of Fine-Tuning Convolutional Neural Networks for HEp-2 Image Classification
## 2 Literature Review
The problem of classifying the intensity of fluorescence has certainly been little addressed [7]. The reason is probably due to the lack of public databases containing both positive and negative images; to date, it seems that the only public database of HEp-2 images with these characteristics is AIDA [4]. In [PERSON] et al. [31], the problem of fluorescence intensity analysis was addressed but the authors did not make a classification between positive and negative, rather between positive, negative and weak positive. The authors extracted features through an Invariant Scattering Convolutional Network and used SVM classifiers with a Gaussian kernel. The network was based on multiple wavelet module operators and was used as a texture description. To classify the tree-class the authors applied the one-on-one approach and trained the tree binary classifier (negative vs. positive/negative vs. weak positive). Their method was trained on a private database of 570 wells for a total of 1771 images in which the fluorescence intensity was blindly classified by two doctors. The accuracy reported in the private database was 89.5%. A classification of fluorescence intensity into positive vs. weak positive was also carried out by [PERSON] et al. [32]. The authors used local contrast features at multiple scales and a KNN classifier to characterize the image, thereby achieving an accuracy of 85% in fluorescent intensity discrimination. In Bennam Elgaiaed et al. [4] the authors have implemented a method based on SVM to classify the HEp-2 images in positive or negative intensity. They achieved an accuracy of 85.5% using traditional features based on intensity, geometry, and shape. The same set of tests was analyzed by two young immunologists verifying a greater ability of the automatic system (85.5% vs. 66%). Other authors addressed the classification in positive/negative intensity on private databases. [PERSON] et al. [33] used a private database with 914 images to train a classifier able to discriminate between positive and negative intensity. Some areas of interest, called patches, have been extracted from the training set with the aid of the scale-invariant feature transform (SIFT) algorithm; therefore, 19 features are extracted from these. The features were extracted from first and second order gray level histograms. Two method of feature reduction was applied, the principal component analysis (PCA) and the linear discriminant analysis (LDA). Finally, the classification was based on the Gaussian mixture model and reaches an accuracy of 89.49%. In the work of [PERSON] et al. [34] the authors presented a fluorescence intensity classification method in which private databases are analyzed. The method makes use of the fusion of global and local type features. For simple cases, they use the SVM classifier with global features, while for doubtful cases, they proposed a further classification based on local features combined with another SVM. The results show an accuracy of 98.68%. However, as the analysis was conducted on a private database, the work does not allow easy performance comparison. Despite its recognized potential, deep learning, and, in particular, the fine-tuning training method, has hardly been used for fluorescence intensity analysis. Only in one of our recent works presented at congress [35], was the fine-tuning method used with the pre-trained GoogLeNet network. In this study, albeit with a very limited analysis, promising results were obtained. In fact, the accuracy was 93% with an area under the Receiver Operating Characteristic (ROC) curve (AUC) of 98.4%. A synthetic representation of the literature presented here, with its pros and cons, is shown in the Table 1.  
In the present work we analyzed the effectiveness of the fine-tuning training method applied to the problem of fluorescence intensity classification in HEp-2 images. For this purpose four of the best known pre-trained networks (AlexNet, SqueezeNet, ResNet18, GoogLeNet) have been analyzed.