# Performance of Fine-Tuning Convolutional Neural Networks for HEp-2 Image Classification
## 3 Materials and Methods
### Convolutional Neural Networks  
The use of Deep Neural Networks for solving classification problems has grown significantly in recent years. In particular, Convolutional Neural Networks (CNN) are widely used [37]; the name derives from the convolution operations present in them. The success of deep learning and CNNs is certainly due, in addition to the high classification performance demonstrated by these classification methods, also to the ease of carrying out a classification process using these tools. In fact, the traditional chain composed of preprocessing, features extraction, training model, is entirely replaced by CNNs, which in their training process include feature extraction. CNNs are networks specialized in data processing which have the form of multiple vectors with a known grid-form topology. An example of this type of data can be a time series, which can be seen as a grid at a size sampled from regular intervals, or an image, which can be seen as a two-dimensional grid of pixels containing the intensity value for the three color channels (RGB).  
The architecture of a CNN is structured as a series of representations made up of two types of layers: the convolution layers and the pooling layers (see Figure 2). The units in the convolution layers are organized into feature maps, in which each unit is connected to a local portion of the map of the next layer through a set of weights called a filter bank. The result of this local weighted average is then passed through a nonlinear function such as Rectified Linear activation Function (ReLU). All units in a feature map share the same filter bank.  
Figure 2: General scheme of the architecture of a the Convolutional Neural Network (CNN).  
The capacity of a CNN can vary based on the number of layers it has. In addition to having different types of layers, a CNN can have multiple layers of the same type. In fact, there is rarely a single convolutional level, unless the network in question is extremely simple. Usually a CNN has a series of convolutional levels, the first of these, starting from the input level and going towards the output level, is used to obtain low-level characteristics, such as horizontal or vertical lines, angles, various contours, etc. The levels closest to the output level produce high-level characteristics, i.e., they represent rather complex figures such as faces, specific objects, a scene, etc. Since the design and training of a CNN is a complex problem, where exhaustive research cannot be used, and training requires large and accurate databases for training, and a computing power that is not always accessible to researchers, a well-established way in the literature is to use pre-trained CNN networks. Thanks to ImageNet competition, extremely powerful CNN networks have emerged [38]. These CNNs have been trained on over a million images to model generic feature rich representations.  
In this work, CNNs were used to classify the fluorescence intensity in HEp-2 images. In detail, the performances of four of the most well-known pre-trained CNNs were analyzed applying them according to the fine-tuning learning approach. The strategy according to which pre-trained networks are used as feature extractors was also evaluated. In this case, the classification phase is usually carried out in combination with a classifier. For this purpose, we have used linear SVM classifiers in this work. The choice of best configuration was performed automatically, using the AUC as a figure of merit in a validation process based on the k-fold method.