# Deep CNN for IIF Images Classification in Autoimmune Diagnostics
## 2 Materials and Methods
### Deep CNN  
In recent years, deep learning networks have allowed significant improvements in classification performance for many problems of pattern recognition and in fact representing in many areas the state of the art of research.  
The term \"deep\" usually refers to the number of layers hidden in the neural network. Traditional neural networks contain only one to two hidden layers, while deep networks can contain up to 150. Deep learning models are trained using large labeled data sets and neural network architectures that learn features directly from data without having to manually extract them.  
One of the most common types of neural networks is known as a convoluted neural network (CNN or ConvNet). A CNN conveys the characteristics learned with the input data and uses the convolutional layers in 2D, which make this architecture suitable for 2D data processing, such as images. CNNs eliminate the need for manual feature extraction [33; 34; 35; 36; 37], so the user does not have to identify features used for image classification. In fact, it is possible to use the power of the pre-trained networks, without investing time and effort in training, to implement the extraction phase of the characteristics. Feature extraction can be the fastest way to use in-depth learning. The operation of CNN is based on the extraction of the features directly from the images. The automatic extraction of the features allows a high precision of the deep learning models intended for artificial vision activities, such as the classification of objects.  
In this work it was decided to use one of the best known CNN (Convolutional Neural Network) networks: AlexNet [28]. This network was trained on the ImageNet database [38] consisting of more than a million images with 1000 object categories. The architecture of the pre-trained network used is shown in Figure 3. The peculiar characteristics of this network are briefly described below: The network architecture consists of eight layers of depth. The first layer accepts an RGB input image of size \(227\times 227\). The first five layers are convolutional (some of which are followed by max-pooling layers) while the last three are fully connected layers with a final 1000-way softmax. AlexNet uses a ReLU (rectified linear unit) layer that consists of a simple activation function such as the max(0,x) thresholding at zero that is faster than the traditional sigmoid. Furthermore, to avoid the problem of overfitting, dropout layers in the fully-connected layers are used as a regularization method.  
Since the network has been weighed and trained to classify a large variety of objects, the feature representation is very robust. In particular, the first layers of the network express lower-level features  
Figure 3: AlexNet architecture used in this work for patterns classification.  
that are gradually elaborated by the deeper layers, refining them into higher-level features. In this work, the sub-images containing the cells to be classified have been appropriately rescaled to acquire the correct dimensionality for the network entrance (\(227\times 227\))  
The vector of features extracted from the pretrainded CNN are used in the training of six support vector machine (SVM) of linear type as specified in Section 2.5. Different layers of the AlexNet network have been evaluated as feature extractors for the problem of the classification of HEp-2 image patterns and the best configuration has been identified.