## 2 Related Work
Since there are 26.7% more abnormal fundus images than normal ones, this imbalance makes the system's final decision more biased towards the abnormal class. In a subsequent work, [PERSON] et al. [22] present another CAD system to identify three retinal diseases, AMD, DR, and glaucoma. The bag-of-visual-words approach and Gaussian mixture model (GMM) were carried out on the training dataset to build the vocabulary. Random forest (RF) was employed as a classification model. However, aside from the fact that the system was tested on a private dataset of images, a requirement for reproducibility, the final results are somewhat disappointing. The reason why the results are not impressive could be because the system depends on the extracted vocabulary from image patterns, not from the image pixels themselves. In addition, the final results are provided on an aggregate basis, i.e., for all three classes of cases, DR, glaucoma and normal. ANN in general and deep learning in particular have also been used heavily for ophthalmic diagnosis using retinal fundus images. An overview of the applications of deep learning of this topic is presented in [25], where the authors describe image datasets that can be used for deep learning purposes, and applications of deep learning for segmentation of the optic disc, optic cup, and blood vessels as well as detection of lesions. A retinal CAD system based on deep residual NN classifiers using small labeled images is presented in [17]. The system employs three tasks related to eye disease diagnosis. The first is identifying five broad categories, DR, AMD, glaucoma, melanoma, and normal images. The second is predicting one of the 320 fine-grained disease sub-categories (grades). The third is creating a textual explanation for the diagnosis. Experimenting on a dataset of 7212 labeled and 35,854 unlabeled images from 3502 patients, the system achieved an overall accuracy of 83%, 75%, and 48% for the three tasks, respectively, which are low compared to the results of the present work. A medical image analysis based on deep mining for screening DR is proposed by [PERSON] et al. [26], aiming to detect the four different DR lesion types in fundus images. Specifically, a deep convolutional NN with 26 different layers acts to automatically detect pathological features. A combined convolutional NN and recurrent NN were developed for heightened glaucoma detection [27]. The CNN/RNN combined model attained an average \(F\) score of 96.2% in identifying glaucoma. This system suffers, however, from low accuracy if the number of training images is not large enough. A retinal CAD system based on a deep learning technique is presented by [PERSON] et al. (2018). A transfer deep learning architecture depends on a residual NN, including feature attention and channel re-calibration to extract features from the retinal fundus image. RGB Kaggle fundus images were used after pre-processing steps to mitigate noise effects of the background. This system resulted in 87.60% for the area under the curve (AUC) of the receiver operating characteristic (ROC) and 59.94% for overall accuracy. This system is highly robust, but the computational cost is relatively high. In ([PERSON] et al., 2018), a deep learning system is proposed to study its efficacy in diagnosing and grading glaucomatous optic neuropathy (GON) through retinal fundus images. The network comprises twenty-two layers, of which eleven are inception-v3 architecture modules. Out of 70,000, 48,116 fundus images were selected to be annotated by ophthalmology doctors (twenty-seven doctors) for labeling as GON images or not GON images. A minibatch gradient descent of a size of thirty-two was used for training using an Adam optimizer, with an initial learning rate of 0.002. The final results achieved for the system were 0.929, 0.956, and 0.920 for accuracy, sensitivity, and specificity ([PERSON] et al., 2018). A multi-layer perceptron (MLP) NN and hand-crafted features are presented by [PERSON] et al.