# Deep Convolutional Neural Network for HEp-2 Fluorescence Intensity Classification
## 1 Introduction  
Autoimmune diseases are several chronic disorders, and there are over 80 different types, some of which are disabling. Antinuclear antibodies are significant biomarkers in the diagnosis of autoimmune diseases in humans, which is performed by means of Indirect ImmunoFluorescence (IIF) test with human epithelial cells (HEp-2 cells) as antigens. The evaluation of antinuclear antibodies (ANAs) consists of the analysis of the fluorescence intensity and the staining patterns. IIF is a test having high sensitivity, but only analytical and not diagnostic specificity, since the positivity for ANA does not automatically confirm the presence of autoimmune disease; indeed, the ANA may be present even in healthy subjects [1, 2]. In the past years, a great deal of effort was put into research regarding Indirect Immunofluorescence techniques, with the aim of development of computer-assisted diagnosis (CAD) systems [3].  
In the clinical practice, IIF samples are categorized into a specific number of levels based on the visual assessment of their fluorescent intensity compared to a set of negative and positive controls.  
As it is aimed at identifying the patient's positivity or negativity to the test, the fluorescence intensity classification phase is very important. Moreover, as regards the CAD system, it will be the result of this phase to establish (in the case of positive output) if the execution of the analysis steps aimed at identifying the staining patterns present in the image will be carried out. Figure 1 shows examples of each class.  
In the literature, there are few scientific works on the automatic analysis of fluorescence intensity in IIF images, and to date, to our knowledge, no article has been published with reference to a public database.  
[PERSON] et al. [4] presented a method, ANAlyte, which is able to characterize IIF images in terms of fluorescent intensity level and fluorescent pattern without any user-interactions. They obtained overall accuracy of fluorescent intensity around 85%.  
[PERSON] et al. [5] have optimized and tested a CAD system on HEp-2 images, which is able to classify the fluorescence intensity. The system classifies positive and negative images using one support vector machine (SVM) classifier. Results showed 85.5% accuracy in intensity fluorescence detection.  
In one of our previous works [6], the analysis of fluorescence intensity has been addressed. The goal has been achieved by performing a preprocessing phase for the image, extracting a considerable number of features, and implementing an SVM classifier. To achieve a reduction in complexity and an appropriate selection of features, the linear discriminant analysis (LDA) method was used. The results obtained show an accuracy of 87% and an A\({}_{\text{Z}}\) area under the receiver operating characteristic (ROC) curve equal to 91.4%.  
In recent scientific research on pattern recognition, Convolutional Neural Networks (CNNs) have been proved to be efficient and reliable models to achieve remarkable performance for image classification and object detection tasks [7]. Moreover, it has been demonstrated that pre-trained CNN architectures can play an important role in terms of features extractors, and allow high classification performance.  
In order to address the fluorescence intensity classification, in this work, a method based on deep CNN is implemented. The distinctive appearance differences between image classes are represented through the learned features from pre-trained CNNs. The positive/negative image classification is carried out using a support vector machine classifier (SVM).  
Figure 1: Indirect ImmunoFluorescence (IIF) images with different fluorescence intensity. At the top are two positive examples, below are two negative examples.