## 1 Introduction  
Eye disorders can lead partial or even total loss of vision, significantly affecting life quality [1]. In order to ensure timely medical intervention and treatment, their early detection relies on the analysis of retinal components such as optic disc (_OD_), macula (_MA_), blood vessels (_BVs_), exudates (_EXs_), and hemorrhages (_HEs_). Only intelligent processing of a retinal image can provide real measures of these specific ophthalmological zones. Adding complex image processing to dedicated neural networks and efficient descriptors and classifiers, the system for determining the Rols in retinal images can be considered as an effective support for decision-making in early disease diagnosis. The system's input is the retinal image and the output is the degree of retinal RoI damage. Automatic detection based on specific imaging devices and real-time image processing can be used for the early-stage decision of an ophthalmologist. Computer-aided diagnosis in an intelligent system also has the advantages of computational power, speed, and continuous development. Many different approaches for retinal RoI identification and evaluation are found in the literature. However, accurate and simultaneous detection, localization, and evaluation of _OD_, _MA_, _BV_, _EX_, and _HE_ is a difficult task for such a system. The general difficulty for simultaneous detection of retinal RoIs is the symmetries between them, like between \(OD\) and \(EX\) (color and, sometimes, segmented shapes) and between \(EX\) and \(HE\) (segmented shapes). It must have intelligent algorithms for image processing and interpretation and sufficient computational power. Usually, it must exclude the similarities (symmetries) and point out the differences. The first RoI, the \(OD\), is the easiest to detect, due to its shape, colors, size, and structure. Therefore, many papers are related to its detection and localization [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14]. For example, the maximum number of decisions from a set of detectors can be used to detect the center of the \(OD\)[7]. By selecting some textural features and image decomposition in overlapping boxes, \(OD\) detection becomes more simple and efficient [4]. Taking into account the density of large blood vessels in the \(OD\) region, the authors in [6; 8] proposed fast and accurate methods for \(OD\) detection. Considering that the \(OD\) has the highest radial symmetry, a methodology based on the radial symmetry detector and separation of vascular information is used in [14] for \(OD\) localization. Sometimes, the \(OD\) and \(EX\) are similar in color, intensity, and shape. In pathological images, a large \(EX\) can be similar to the \(OD\). To overcome this inconvenience, an automatic \(OD\) detection is proposed in [13], based on the symmetry axis of blood vessel distribution in this region. Some authors remove the \(OD\) before the detection and localization of \(EX\)s. To this end, the authors in [15] proposed a methodology to differentiate \(EX\)s from \(OD\)s by considering the features on different color components. The optic disc is identified as a region where the majority of pixels have a great difference on R and G channels. Next, a thresholding method is applied in order to extract the exudates from the obtained binary image. Some features like mean intensity, entropy, uniformity, standard deviation, and smoothness are computed for obtaining an improved accuracy. The mentioned characteristics have different values for \(EX\)s and \(OD\)s; so, \(EX\)s can be identified by removing the areas that do not have matching values of the previous characteristics. For the optic disc detection, in [16], an algorithm based on the 2D Gaussian filter and blood vessel segmentation was implemented. Also for \(OD\) detection [PERSON] et al. [17] applied a modern algorithm based on ant colony optimization and anisotropic diffusion.