## 1 Introduction  
The macula, which is located in the central part of the retina, is the most sensitive area of vision. Its healthiness can be affected by a number of pathologies such as AMD and DME. As technology advances, OCT has been extensively used to capture the textural and morphological variations in the retina [1; 2; 3]. The ability to provide cross-sectional images of tissue makes it possible to diagnose AMD and DME [4; 5; 6]. A majority of the previous works on the retina focused on the methods of retinal layer segmentation [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22]. During the past decades, a multitude of classification algorithms were proposed to facilitate the automatic detection of retinal disorders (especially AMD and DME). [PERSON] et al. [23] proposed a methodology for computing the Local Binary Pattern (LBP) to encode the texture and shape information in retinal OCT images, and represented images using a multiple-scale spatial pyramid followed by a principal component analysis for dimension reduction. [PERSON] et al. [24] segmented the OCT images to find the retinal pigment epithelium (RPE) layer and used a binary classification to classify between AMD and DME. [PERSON] et al. [25] utilized multiple-scale histograms of oriented gradient (HOG) descriptors as feature vectors of a support vector machine (SVM)-based classifier. [PERSON] et al. [26] extracted five distinct features (three thickness profiles of the sub-retinal layers and two cyst fluids within the sub-retinal layers) from the images and used SVM for classification. [PERSON] et al. [27] took advantage of sparse coding and dictionary learning based on scale-invariant feature transform (SIFT) descriptor for automated detection of AMD, DME, and normal (NOR). [PERSON] et al. [28] proposed an unsupervised feature learning approach which extracted a set of small descriptive image patches from the training data and used the patches to create a patch occurrence histogram for each image. [PERSON] et al. [29] systematically selected the linear configuration pattern (LCP) based features of the OCT images by the Correlation-based Feature Subset (CFS) selection algorithm. All the aforementioned approaches mainly consist of two components, namely extracting features and training classifier. The emergence of deep learning brings new methods and ideas to the classification of retinal OCT images. Especially the development of the CNN has had an important impact on images classification [30]. The CNN makes it possible to process images in the form of pixels as input and to give the desired classification as output, which can avoid the complicated feature extraction in the traditional algorithm. [PERSON] et al. [31] fine-tuned (fix the weights in the lower levels and retrain the weights of the upper levels with back propagation) the GoogLeNet [32] to identify retinal pathologies on spectral domain OCT (SD-OCT) images. In the same year, [PERSON] et al. [33] proposed a multiple-scale convolutional mixture of expert ensemble model which applies CNN on multiple-scale sub-images. Recently, [PERSON] et al. [34] proposed an image-based deep learning (IBDL) method, which fixed the weights of the network and was used as a feature extractor. All of the methods mentioned above are used for classifying OCT B-scans, although the method in Ref. [25] could classify three-dimensional (3-D) retinal OCT images. Nevertheless, [PERSON] et al. [35] first proposed a method based on decomposition and graph analysis which could directly deal with the 3-D OCT volume image. More recently, [PERSON] et al. [36] presented a method utilizing the principal component analysis network (PCANet) to extract the features from each B-scan of the 3-D retinal OCT images and fusing multiple kernels as a composite kernel to exploit the strong correlations among features of the 3-D OCT images for classification. CNN is widely used in computer vision (especially in image classification) since the ImageNet [37] competition in 2012.