# Performance of Fine-Tuning Convolutional Neural Networks for HEp-2 Image Classification
## 1 Introduction  
Autoimmune diseases occur whenever the immune system is activated in an abnormal way and attacks healthy cells, instead of defending them from pathogens; thus, causing functional or anatomical alterations of the affected district. [1]. There are at least 80 types of known autoimmune diseases (AD) [1]. AD can affect almost any part of the body i.e., tissue, organ, body system [2]. The anti-nucleus autoantibodies (ANA) are directed towards distinct components of the nucleus and are traditionally sought after with the indirect immunofluorescence (IIF) technique. With this method, in addition to the antibodies directed towards nuclear components, antibodies directed towards antigens are also highlighted and located in other cellular compartments. When Human Epithelial type 2 (HEp-2) cells are used as a substrate, the IIF method allows the detection of autoantibodies to at least 30 distinct nuclear and cytoplasmic antigens [3]. For this reason, the IIF technique with HEp-2 substrate isconsidered the gold standard test for the diagnosis of autoimmune diseases. The patterns with which fluorescence can occur are associated with groups of autoimmune diseases. In the IIF image analysis flow, the first phase concerns the fluorescence intensity classification. This phase is obviously of considerable importance in the diagnostic workflow, as it aims to discriminate on the presence/absence of autoimmune diseases. Figure 1 shows examples of positive/negative class. However, the analysis of IIF images, as a whole, and in particular, in regards to the analysis of intensity, is extremely complex and linked to the experience of the immunologist [4]. For this reason, in recent years there have been numerous scientific works aimed at obtaining automatic support systems for HEp-2 image analysis [5; 6]. In particular, in the last few years, various research groups interested in the topic have tried to exploit the potential offered by recent machine learning techniques in order to address the problem of the classification of HEp-2 images. However, the topic of fluorescence intensity classification is still poorly addressed [7]. Over the past decade, the popularity of methods that exploit deep learning techniques has considerably increased, evidently as deep learning has improved the state-of-the-art methods in research fields, such as speech recognition and computer vision [8]. Although neural networks had their scientific boom already in the 80s, their recent success can be attributed to an increased availability of data, improvements in hardware/software [9], and new algorithms capable of both speeding up learning in the training phase and improving the generalization of new data [10]. In the field of computer vision, deep learning has expressed its potential in image processing thanks to Convolutional Neural Networks (CNNs). In recent years there have been several fields of use of CNNs that, nowadays, allow the development of important applications. Some examples are: the antispoofing of the face and iris [11], recognition of highway traffic congestion [12], the image steganography and steganalysis [13], the galaxy morphology classification [14], drone detection, and classification [15; 16]. Figure 1: Examples of indirect immunofluorescence (IIF) images with different fluorescence intensity: in the upper left a positive centrioles image, in the upper right a positive cytoplasmic image, in the lower left a positive nucleolar image, in the lower right a negative image. The positivity/negativity of fluorescence is not strictly correlated with the luminous intensity of the Human Epithelial type 2 (HEp-2) images; the image in the upper left for example, although presenting low light intensity, has a positive fluorescence intensity. Conceptually, the CNNs are inspired by the visual system as proposed in the works of [PERSON] and [PERSON] on cat and monkey visual cortices [17]. A CNN accepts an image directly as input and applies a hierarchy of different convolution kernels to it.