## 1 Introduction
More recently, the authors in [18] proposed a supervised model for \(OD\) abnormality detection for different datasets, taking into account a deep learning approach from global retinal images. The results show a good accuracy and indicate potential applications. Automatic image analysis methods aiming to detect and segment \(MA\) and fovea are currently based on support vector machine classification [19], hierarchical image decomposition [20; 21], statistical segmentation methods [22], deep learning [23], and pixel intensity characteristics [24]. The authors in [7] presented a method which combines different \(OD\) and \(MA\) classifiers to benefit from their strong points (high accuracy and low computation time). With the purpose of detection of macular degeneration, [PERSON] et al. [25] proposed some algorithms to localize and segment the \(MA\). [PERSON] et al. [26] addresses blood vessels localization, segmentation, and their spatial relations in order to detect the optic disc and macula. Based on pixel intensity (low), the authors in [27] also proposed the \(MA\) detection technique. They also proposed an algorithm for the \(OD\) diameter estimation. [PERSON] et al. [28] presented a system based on artificial intelligence for evaluation of macular edema to support the ophthalmologists in early detection of the disease. They used a comprehensive characteristics group and a Gaussian-mixture-model-based classifier for accurate detection of \(MA\). [PERSON] et al. [29] detect macula from an autofluorescence imaging device in connection with \(OD\) position. This automatic positioning is based on a top-down approach using vascular tree segmentation and skeletonization. In [30] the authors proposed a method to detect and localize the macula center using two steps: first, the context - based information is aggregated with a statistical type model in order to obtain the approximate localization of the macula; second, the aggregation of seeded mode tracking is used for final localization of the macula center. Over time, many methods and approaches have been used for the detection of \(EX\)s (e.g., thresholding methods considering the green channel [31; 32; 33]). As a consequence that the illumination conditions are different, image preprocessing algorithms for correction are necessary. [PERSON] and [PERSON] [34] proposed an algorithm for \(EX\) detection which first identifies and rejects the healthy areas based on their characteristics and second segments the \(EX\)s from the remaining regions using a region growing method. [PERSON] et al. [35] presented a hierarchical procedure for \(EX\) detection in eye fundus images to solve some difficulties such as the recognition of similar-colored objects (e.g. cotton wool spots, optic disc, and the normal macular reflection). Using an artificial intelligence method, an automated identification of \(EX\)s is obtained in [36]. After an initial primary processing phase, an image segmentation based on fuzzy clustering is used and a dataset of regions of interest is obtained. For each area, characteristics such as size, color, edge, and texture are extracted. The authors proposed a deep neural network, based on the extracted characteristics for the detection of areas containing \(EX\)s. The images are firstly filtered by applying a fuzzy clustering technique. Furthermore, the method in [37] localizes and evaluates the exudates, taking into account different image processing techniques, morphological filters, and image features. The approaches for \(HE\) identification commonly used can also be grouped into different categories such as thresholding, segmentation, mathematical morphology, classification, clustering, neural network, etc. For example, [PERSON] et al. [38] described and validated a method for the identification of micro-aneurysms and \(HE\)s in retinal images, which can be used in the diagnosis and grading of diabetic retinopathy.