## 2 Related Works  
This section presents the main works related to studying VL in images: introducing the objective of each work, some techniques used, image databases, and results achieved. [PERSON] (2011) [18] proposes a method to automatically determine the infection levels in microscopy images infected with the parasite _Leishmania_. For this purpose, the authors used computer vision and pattern recognition techniques. The images are subjected to a pre-processing step to normalize the lighting conditions. Then, the method performs the detection of _Leishmania_ macrophages and parasites using Otsu [19] threshold techniques. Due to the overlapping of segmented regions, the authors used a rule-based statistical classifier [20] to divide these segmented regions. Finally, the sub-regions were classified using a Support Vector Machine (SVM) [21], defining the infection levels. In the work of [PERSON] and [PERSON] (2012) [22; 23], the authors developed automated methods to identify cells and parasites in microscopic images, allowing for more precise annotations. These authors used segmentation through the Otsu threshold, and then the segmented regions were divided into sub-regions using a rule-based classifier. The authors used a trained SVM with data extracted from several Gaussian Mixture Models (GMM) [24] of the segmented sub-regions. The accuracy of the methods was above 90%, with an approximate 85% accuracy in the individual detection of parasites in regions with multiple cores. [PERSON] (2013) [25] developed an automatic method for annotating _Leishmania_ infections, using the K-means [26] grouper. The strategy primarily relied on detecting blobs, which are shapes that differ from other regions in the image, such as clear or dark circular forms [27]. Additionally, the authors performed cytoplasm segmentation, clustering, and separation by utilizing concave regions of the cell contours. This method was compared with the method by [PERSON] (2012) [28] and obtained an F1-Score 8% higher in the detection of parasites. In the work by [PERSON] (2014) [12], the authors developed a method to determine the positions of infected macrophages and parasites. In addition to detection, the authors separated overlapping cells based on contour concavity. This method is also based on detecting, grouping, and separating blobs using concave regions of cell contours, as in [PERSON] (2013) [25]. In comparison with the works by [PERSON] (2011) [18] and [PERSON] (2012) [28], the authors conclude that the proposed method achieves an F1-Score up to 6% better in the annotation of infections by _Leishmania_. In [PERSON] (2014) [29], the authors presented an automatic method for segmenting parasites. Due to the high contrast in images of this nature, it was possible to use a color-based segmentation to remove the background of the images using the K-means grouper. The proposed method was evaluated in 40 images, proving reliable and robust in terms of results. As a continuation of the research, [PERSON] (2016) [30] added a pre-processing step to correct lighting non-uniformities, a primary segmentation step based on the Watershed [31] edge-detection algorithm, and a region-merging step using combined criteria of region homogeneity and edge integrity as a solution to the over-segmentation produced by the initial processing step. In work by [PERSON] et al. (2018) [32], the authors presented an automatic image analysis method for counting _Leishmania_ parasites. The work applied classical algorithms to segment and outline the image to identify parasites. The authors used 382 private images in this process, resulting in an infection rate of approximately 80%. In work by [PERSON] et al. (2018) [17], the authors presented an unsupervised analysis method for detecting leishmaniasis parasites in microscopic images. They implemented Deep Learning techniques and trained a U-Net [33] model to segment _Leishmania_ parasites and classify them into amastigotes, promastigotes, and adherent parasites. In [PERSON] et al.