Then the white lesions and red lesions were detected through modification of their previous work presented in [7, 8] respectively. Finally, the detected lesions were classified based on a waterfall model based classification. Rates of 80% and 50% for sensitivity and specificity were reported respectively. Figure 2: Representative pathological signs of DR: (**a**) mild non-proliferative diabetic retinopathy; (**b**) severe non-proliferative diabetic retinopathy; (**c**) neovascularization; (**d**) vitreous hemorrhages; (**e**) fibrous proliferations and (**f**) laser scars after treatment. [PERSON] et al. [9] presented a DR screening system based on morphological component analysis. Without using any segmentation methods, the structural information was exploited to evaluate performance. The blood vessels and lesions were then separated using morphological component analysis. Finally, a support vector machine was employed to distinguish healthy retinal images from abnormal ones. It achieved an accuracy of 92.82%, sensitivity of 92.01% and specificity of 95.45%. [PERSON] et al. [10] filtered the retinal healthy images from DR based on the result of exudates detection using the features of the sub-images locally as the input to multiple classifiers. The paper obtained sensitivity of 92% and specificity of 91%. A grading system for DR was presented by [PERSON] et al. [11] which used the number of each detected DR lesion as determined through morphological operation. [PERSON] et al. [12] presented the automatic analysis of fundus images for modeling early changes in retinal blood vessels resulting from diabetes based on the total number of branching angles and average acute branching angles in addition to patient demographic information. Linear regression was applied in this study. Similarly, [PERSON] et al. [13] proposed a method to detect BV for the diagnosis of DR. Taking into account the works mentioned above and the methods in Reference [10], the authors attempted to detect only the EXs while other DR lesions including MIs, HMs, and CWSs were ignored. When ignoring other associated lesions, the detection system is not robust. The papers in [6; 9] filtered any abnormal signs of DR using all extracted features whether significant differences existed or not. The use of all features without the use of feature selection may lead to heavy computation time, redundancy, and poor predictive performance. The papers in [4; 5] applied a feature selection to select the optimal feature set to feed to the classifiers. However, more feature selection methods should be studied and the results compared. In this paper, we proposed an automated DR screening system based on five processing steps: (i) preprocessing, (ii) lesion detection, (iii) feature extraction, (iv) feature selection and (v) classification. Image preprocessing is completed first so as to standardize image size and improve retinal image quality. Then, bright and dark lesions are segmented using a combination of morphological-top-hat and Kirsch edge-detection methods using a Boolean operation. Post-processing is further applied to filter out only DR lesions. After the DR lesions are segmented, eight feature sets with a total of 208 features are extracted, and hybrid simulated annealing is utilized to select the optimal feature set for input to the ensemble bagging classifier. The rest of the paper is organized into four sections. Section 2 outlines the materials and methods used in the study. The experimental results are described in Section 3. Sections 4 and 5 present the discussions and conclusions, respectively.