## 1 Introduction
Their contribution is a set of dynamic shape features which show the evolution of shapes under a processed image and allow differentiation between lesions and blood vessels. The method was tested against six image datasets, both public and private. A splat-based feature classification algorithm with applications to large, irregular \(HE\) detection in retinal images was proposed in [39]. For detection and segmentation of retinal RoIs, different classifiers were used: model-based (template matching), minimum distance between vector features, voting scheme, and, recently, neural network (NN). [PERSON] et al. [40] proposed an algorithm for computer - based identification of the lesions due to the diabetic retinopathy. The algorithm is integrated into a system in order to support the ophthalmologist decision in the detection and monitoring of the disease. The authors analyze the performance of three different NN classifiers in automated detection of lesions or exudates: support vector machine, radial basis function, and multilayer perceptron. By applying different criteria and testing on 67 images, they conclude that the multilayer NN classifier based on image criterion has the best results. This research was extended in [41], where new criteria and classifiers (e.g., logistic regression) were tested against those in the previous paper. Now, the relationship between the extracted set of characteristics and the class of objects is highlighted. Again, the multilayer perceptron performed the best out of the selected methods, but only by a small margin in comparison with logistic regression. Moreover, in [42] a new neural network supervised approach for blood vessel detection was proposed. The proposed algorithm uses an NN architecture for pixel classification and creates a vector of moment-invariants-based and gray-level features for pixel representation. The performance of the algorithm was tested on STARE [43] and DRIVE [44] datasets, with better performance than other existing solutions. The authors in [45] used CNNs (Convolutional NNs) to detect hemorrhages. They tried to simplify and speed it up by heuristically classifying samples of the training phase. The authors in [46] presented a methodology and their implementation which is based on a voting procedure with the aim of raising the decision accuracy. They proposed the technique of majority voting and probability approach to evaluate the correctness of decision. The important goal of the work consisted in solving detection problems in image processing algorithms which are geometrically constrained. For example, the algorithm performance was tested with good accuracy for \(OD\) identification on the MESSIDOR dataset [47]. In order to increase the performances of classifications, the voting scheme and NN approaches were combined in [48]. In this case, two connected NNs were considered: one for feature selection and the other for classification, based on local detectors. Finally, a voting scheme with different weights for local classifiers was used, taking into account the results from associated confusion matrices. In order to increase the efficiency of the classifiers, primary processing of retinal images is necessary. Therefore, [PERSON] et al. [49; 50] presented algorithms for identification and monitoring glaucoma and diabetic retinopathy, using primary processing l (histogram equalization, filtering, and morphological operations). For a particular disease, specific characteristics are established. Based on these features, an artificial NN classifier identifies the presence of a particular disease and its evolution. The authors in [51] introduce an algorithm based on morphological operations for the identification of micro-aneurysms and _HE_s in retinal images. The image is firstly primary processed and then, important regions of interest such as _OD_, blood vessels, and fovea are successively removed from the retinal image. Only the _HE_s and micro-aneurysms remain. The obtained results are similar to those of other papers as sensitivity and specificity.