## 2 Related Work
(2018), using retinal fundus images to diagnose DR by searching pixel by pixel in the retinal image for biomarkers of DR lesions. The system uses 24 hand-crafted features and a 3-layer NN. A post-processing technique depending on mathematical morphological operators is used to optimize the blood vessel segmentation procedure. A selected vector of features is proposed in this study. In comparison, three publicly available datasets are used. The experimental results, visually and quantitatively, indicate the robustness of the suggested methods. The proposed method gave 0.960, 0.754, and 0.984 for the DRIVE dataset, 0.963, 0.780, and 0.982 for the STARE dataset, and 0.957, 0.758, and 0.984 for the CHASSE_DB1 dataset, for accuracy, sensitivity, and specificity, respectively. Despite the robustness of this method, it is dedicated to only one unique ocular disease, \(DR\). The use of NNs and deep learning is still the focus of much research in the area of retinal image analysis. For example, a technique based on a gray wolf optimized NN is presented by [PERSON] and [PERSON] (2017) for early recognition of glaucoma using retinal images. They first converted the original color images into gray-level, then the artifacts due to noise were suppressed using an adaptive median filter. The extracted features comprised gray-level co-occurrence matrix (GLCM) features, speedup robust features, histogram of oriented gradient (HOG) features, and global features that were encoded for gray wolf optimization. In ([PERSON] et al., 2018), [PERSON] et al. studied hemorrhage detection based on a 3D CNN deep learning framework and feature fusion for evaluating retinal abnormality in diabetic patients. A pre-trained modified CNN model was employed to extract features to form a feature vector. The feature vector was fused by convolutional sparse image decomposition, and later the best features were selected by multi-logic regression controlled entropy variance techniques. They achieved 0.9771 for the average accuracy. [PERSON] et al. (2018) proposed a deep learning system to detect glaucomatous optic neuropathy from retinal fundus images. Their NN comprised 22 layers, with 11 inception-v3 architecture modules. Out of 70,000 images, 48,116 fundus images were selected for annotation by ophthalmologists as GON or not GON. The final results achieved for the system were 0.929, 0.956, and 0.920 for accuracy, sensitivity, and specificity, respectively. In ([PERSON] et al., 2018), [PERSON] et al. studied detection of DR using a fusion of textural and ridgelet features of retinal images and a sequential minimal optimization classifier. The ocular features were extracted and fused based on co-occurrence, run-length features, and ridgelet transform coefficients. The method was tested using sequential minimal optimization through retinal ocular images. The sensitivity, specificity, and accuracy obtained were 0.9887, 0.9524, and 0.9705 for the DIAREKDB1 dataset, and 0.909, 0.91, and 0.910 for the Kaggle dataset, respectively. The problem with deep learning, however, is that it acts mostly as a black box, with plenty of stacked layers, providing little knowledge about local features at the image level. Furthermore, its training requires a huge number of images, which may not be readily available. For example, the work by [PERSON] et al. (2016) depends on 90,000 images from Kaggle and 110,000 images from e-ophtha, in addition to 89 DiaretDB1 datasets. Another problem with deep learning is that it is remarkably time-consuming, primarily during training but also during testing. All these drawbacks are mitigated in the approach followed in the present work which depends mainly on hand-crafted features and conventional ML models.