# Deep CNN for IIF Images Classification in Autoimmune Diagnostics
## 1 Introduction
With the sparse codes, a multiclass linear SVM with a one-versus-all strategy is used. In the work of [PERSON] et al. [20] the authors used local descriptors at different scales and invariant to rotation. The descriptors were obtained from a log-polar grid transformation, from a multiscale smoothing of directional gradients, and from the Fourier transform. The bag of words technique was used in combination with a linear SVM. The I3A Task-1 database was used by the authors. [PERSON] et al. [21] presented a method based on linear local distance coding in which, starting from local features, a local distance vector transformation was used by Euclidean distance. Finally, linear  
Figure 1: Sample cells images of six patterns from different specimens. coding and max pooling were used, both on the local distance vector and on the local features. The concatenations were provided as examples to a linear SVM. In recent scientific research on pattern recognition, deep learning methods and in particular the convolutional neural networks (CNNs) have been proven to be efficient and reliable models to achieve remarkable performance for image classification and object detection tasks [22]. Moreover, it has been demonstrated that pre-trained CNN architectures can play an important role as feature extractors and allow high classification performance. Very recently, depth learning methods have been applied to IIF image classification problems. In the work of [PERSON] et al. [23] the authors addressed the problem of segmentation and classification of IIF images. In particular, they used a variant of the note VGG-16 (16-layer network used by the [PERSON] team) called FCN which is aimed at segmentation. They used CNNs for both segmentation and classification. For the development and the performance test, they used the I3A Task-2 database. [PERSON] et al. [24] used the known CNN AlexNet in combination with an SVM classifier for the classification of cells in mitosis. In our previous work [25] we addressed the problem of intensity fluorescence classification. The problem was faced by analyzing the whole image and starting from it by extracting the features. To this end, several pre-trained networks, used as feature extractors, were analyzed, and the image classification was obtained by training an SVM classifier. [PERSON] et al. [26] used the known CNN VGG-19 to extract features and combine them with local features such as RIC-LBP (rotation invariant co-occurrence local binary pattern) and JML (joint motif labels) for an efficient cell classification. The combination of features was used to train a random forest classifier. [PERSON] et al. [27] proposed a method for analyzing HEp-2 images based on the use of a CNN to construct a pattern histogram, and through this a linear SVM was trained. The CNN used was composed of 10 layers, of which the first nine were convolutional layers while the last was a softmax layer for classification. The system was trained and tested on data from the I3A Task-2 public database. In the present paper a system able to classify the fluorescence patterns is presented. The analysis was conducted both at the cell-level (i.e., in terms of cell images correctly classified) and at image-level (i.e., in terms of IIF images correctly classified). The system uses a pre-trained network, AlexNet [28], as a feature extractor and is able to classify the following six fluoroscopic patterns: Homogeneous, speckled, nucleolar, centromere, Golgi, and nuclear membrane. The classification phase was carried out by developing six linear SVM classifiers with a one-against-all training scheme (OAA). The cell-pattern association was obtained by means of a \(k\)-nearest neighbors (KNN) classifier. Furthermore, in the present work, the effectiveness of the extraction of the features both from the segmented cells (internal) and from the boundary boxes containing the cells was evaluated. The data augmentation method as a tool for improving classification performance was evaluated.