## 1 Introduction  
The retina in human eyes receives the focused light by the lens, and converts it into neural signals. The main sensory region for this purpose is the macula which is located in the central part of a retina. The macula is responsible for the central, high-resolution, color vision that is possible in good light. The retina processes the information gathered by the macula, and sends it to the brain via the optic nerve for visual recognition. Macular health can be affected by a number of pathologies, including age-related macular degeneration (AMD) and diabetic macular edema (DME). They account for the majority of irreversible vision loss subjects in developed and developing countries [1; 2; 3]. In the case of DME fluid is accumulated [4] and in the case of dry AMD drusen is deposited resulting in geographic atrophy, thereby incurring the deformation of retinal layers [5; 6]. Without appropriate treatment, patients of dry AMD may develop choroidal neovascularization (CNV), a severe blinding form of advanced AMD (i.e. wet AMD). In the case of CNV, new blood vessels are created in the choroid layer of the eye. In ophthalmology, one of the most commonly used integral imaging techniques is spectral domain optical coherence tomography (SD-OCT), with approximately 30 million OCT scans performed each year worldwide [7]. OCT is a non-invasive imaging technique, which captures cross-sectional images at microscopic resolution of biological tissues [8]. By providing a clear cross-sectional representation of the retinal pathology in these conditions (Figure 1), OCT is critical to the early identification and treatment of retinal pathologies today. Examples in Figure 1 are picked from a public dataset provided by [9]. Since OCT image interpretation is time-consuming and tedious for ophthalmologists, different computer-aided diagnosis (CAD) systems for semi/fully automatic analysis of OCT data have been developed in the past decade. We now briefly discuss a number of related works on the topic of OCT data classification. In 2011, [PERSON] et al. proposed a methodology for detecting macular pathologies (including AMD and DME) in fovea slices of OCT images, in which they used local binary patterns (LBP) and represented images using a multi-scale spatial pyramid (SP) followed by a principal component analysis (PCA) for dimension reduction [10]. In 2012, [PERSON] et al. and [PERSON] et al. proposed methods for representing images based on a graph [11; 12]. In 2014 [PERSON] et al. presented a method for classifying AMD, DME and healthy C-scans using multi-scale histogram of gradients (HoG) features and a support vector machine (SVM) classifier [13]. In 2017, [PERSON] et al. proposed a classification method based on techniques of scale-invariant feature transform (SIFT), sparse coding (SC), dictionary learning (K-SVD), multi-scale max pooling and linear SVM [14]. In recent years, deep neural networks (DNNs) have outperformed many traditional techniques on the large-scale ImageNet dataset [15]. Researchers designed many networks to obtain high accuracy and reduce parameters, such as AlexNet [16] in 2012, VGGnet [17] in 2014, GoogLeNet [18; 19] in 2015, ResNet [20] in 2016 and DenseNet [21] in 2017. The outstanding performance of DNN inspires scholars to apply them on medical images [22; 23]. To our best knowledge, there are three kinds of methods to apply DNN on medical images. The first one, also the most straightforward, is to initialize randomly all the layers of a DNN and train the DNN on medical datasets. This method requires large-scale medical datasets, which is not always available in real world. The second method utilizes pre-trained DNN as feature extractor, extracts features of medical images and classifies them with softmax dense layer, SVM, random forest (RF) or other statistic classifier. This method is efficient since no training of the DNN is required. The last one is to finetune the DNN pre-trained on ImageNet dataset using medical datasets. Some experiments have been conducted to compare these three methods.