# Performance of Fine-Tuning Convolutional Neural Networks for HEp-2 Image Classification
## 2 Literature Review  
Numerous contributions have been made in recent years by the scientific community in the classification of HEp-2 images, especially due to contests organized on the subject, such as ICPR 2012 [20], ICIP 2013 [21], and ICPR 2014 [22]. However, these competitions have only dealt with the theme of pattern classification, considering 6/7 classes of significant interest. In [PERSON] and [PERSON]'s work [23], a deep cross residual network (DCR-Net) was proposed. The structure is similar to DenseNet, but the authors, in order to minimize the vanishing gradient problem, added cross-residual shortcuts between some layers to the original network. The advantage of the change is to increase the depth of the network by improving feature representation. They made a date augmentation for rotations. The database used is I3A Task 1 and MIVIA. The method showed a Mean Class Accuracy (MCA) of 85,1% on a private test of I3A database. [PERSON] et al. [24] proposed a method that allowed them to win the ICPR 2014 contest with an MCA of 87.1%. They extracted a local feature vector that is aggregated via sparse coding. The method uses a pyramidal decomposition of the cell analyzed in the central part and in the crown that contains the cell membrane. Linear SVMs are the classifiers used on the learned dictionary. Specifically, they use four SVMs, the first one trained on the orientation of the original images, and the remaining three, respectively, on the images rotated by 90/180/270 degrees. In a few cases, the authors have ventured into unsupervised classifications methods for the analysis of HEp-2 images [25; 26]. In these cases, the number of analyzed patterns was reduced. In [25], the authors compared the method called BoW (Bag of Words), based on hand-crafted features and the deep learning model as learning hierarchical features, by clustering unsupervised learning. The authors obtained the best results with the BoW model. They demonstrated that, while the deep learning model allows better extraction of global features, the BoW model is better at extracting local features. In [26], the authors proposed the grouping of centromeres present on the cells through a clustering K-means algorithm; the method allows the identification and classification of the centromere pattern. The method is tested on two public databases, MIVIA and Auto-Immunite, Diagnostic Assiste par ordinateur (AIDA), on which accuracy performances of 92% and 98%, respectively, are obtained. Despite the high indexing performance of the centromeric pattern, being able to classify only one type of pattern is certainly a limitation for the method. Only recently has deep learning been applied to the various fields of HEp-2 image analysis [7]. In this context, one of the first works on the classification of HEp-2 images in which CNN was used is due to [PERSON] et al. [27]. In this work, a CNN having eight layers, compared to the Bag of Features and Fisher Vector methods, was compared. The proposed CNN architecture has six convolution-type layers with pooling operations, and two fully-connected layers. The database used for the test is I3A Task 1 and MIVIA. The authors obtained 96.76% of mean class accuracy (MCA) and 97.24% of accuracy. In [28], the authors used well-known CNNs in the pre-trained mode as vector feature extractors and trained multiple SVM classifiers in the one-against-all (OAA) mode. This strategy is one of the best known for breaking down a multi-class problem into a series of binary classifiers. Instead of using the classic OAA scheme, in which the test is assigned to the majority classifier result, the authors proposed the use of a K-nearest neighbors (KNN) based classifier as a collector of the various ensemble results. The performances on the public database I3A are in terms of MCA 82.16% and 93.75% at the cell level, and at the image level, respectively. Some research groups have dealt with other aspects in the field of HEp-2 images, such as segmentation [29] and the search for mitosis [30].