## 3 Related Work
### Literature Review on Retina Disorder Classification Using OCT  
Although the previous studies present effective segmentation techniques, there is a need to develop comprehensive diagnostic systems that emphasize RoI and perform disease classification. Such systems can help alleviate the burden on medical care systems. Current automated diagnostic systems either use traditional ML techniques of feature extraction, segmentation and classification or utilize deep learning architectures with unprocessed images. An example of these automated systems with OCT imaging can be found in the work of [PERSON] et al. [18]. It described an automated method for detecting retinal diseases using features extracted by Histogram of Oriented Gradients (HOG) techniques and SVMs to classify three classes namely DME, dry AMD and Normal. Their method does not rely on the segmentation of inner retinal layers; however, retinal flattening is performed. The proposed model achieved 100% for both DME and dry AMD classes and 86.67% for normal class. Another example of traditional approach is that of [PERSON] et al. [19]. They proposed a pipeline model that generated a large set of extracted features using HOG and Local Binary Pattern (LBP) at four levels of the multiscale Gaussian lowpass image pyramid. These features were either reduced using Principle Component Analysis (PCA) or directly passed to SVM classifier, which was used to classify two classes: DME and normal. Their best model achieved an accuracy of 81%. [PERSON] et al. [1] addressed the problem of automatic classification of OCT images through SVM (leave one out) technique after image denoising and extraction of features from thickness profile and cyst spaces. This technique was based on extracting features from segmenting the retinal layer (ILM and choroid layer) and from the changes in thickness of these layers. [PERSON] et al. [1] applied that technique for the identification of patients with DME versus normal subjects. The proposed model achieved an accuracy of 79.65%. These traditional approaches [18; 19; 1], which employed hand-crafted features for the classifiers, were shown to obtain promising results. However, these approaches shared a common disadvantage, which is requiring abundant expert knowledge. Medical knowledge was needed to correctly detect retinal layers and/or changes in layers' thickness [1] and/or assist in retinal flattening [18; 19]. Such dependence on experts' availability gives rise to another concern, which is having poor generalization to other domains [20]. Despite these limitations, hand-crafted features remain to provide intuitive information, which enables the needed analysis of the model [21]. Hence, the generation of hand-crafted features that are not heavily reliant on experts' knowledge would provide the required balance of providing explanation of the model without being dependent on an expert. This balance would help mitigate the pressure on medical systems. In order to overcome these disadvantages, deep learning techniques were employed to perform embedded feature extraction implicitly. [PERSON] et al. [22] used a deep learning architecture (VGG16) for extracting features from different layers of the network. Classifications were made using KNN and Decision Tree based on the extracted features. The model is used to classify two classes: DME and normal. The best model got an accuracy of 87.5%. [PERSON] et al. [23] proposed a new CNN architecture for OCT classification. The new CNN consists of two blocks, the first block contains four subblocks with convolutional layers and max pooling and the last block has two fully connected layers. The new CNN was evaluated with different learning rates, until it was able to correctly detect DME with a classification accuracy of 93.75% between two classes: DME and normal at learning rate 0.00001. [PERSON] et al. [24] developed a pipeline model consisting of two CNNs.