###### Abstract  
Visceral Leishmaniasis (VL) is a neglected disease that affects between 50,000 and 90,000 new cases annually worldwide. In Brazil, VL causes about 3500 cases/per year. This chronic disease can lead to death in 90% of untreated cases. Thus, it is necessary to study safe technologies for diagnosing, treating, and controlling VL. Specialized laboratories carry out the VL diagnosis, and this step has a significant automation power through methods based on computational tools. The gold standard for detecting VL is the microscopy of material aspirated from the bone marrow to search forastigotes. This work aims to assist in detecting amastigotes from microscopy images using deep learning techniques. The proposed methodology consists of segmenting the Leishmaniasis parasites in the images, precisely indicating the location of the amastigotes in the image. In the detection of VL parasites, in this methodology, a Dice of 80.4% was obtained, Intersection over Union (IoU) of 75.2%, Accuracy of 99.1%, Precision of 81.5%, Sensitivity of 72.2%, Specificity of 99.6%, and Area under the Receiver Operating Characteristics Curve (AUC) of 86.5%. The results are promising and demonstrate that deep learning models trained with images of microscopy slides of biological material can precisely help the specialist detect VL in humans. computer vision; deep learning; segmentation; visceral leishmaniasis +
Footnote †: journal: Applied  
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female insect vector, called sandflies [6]. In Brazil, the main species responsible for transmission is the _Lutzomyia longipalpis_, which through its hematophagous habit (group of animals or parasites that feed on blood) infects vertebrate hosts, such as rodents, dogs, and humans [7]. In humans, _Leishmania_ can infect different types of cells, mainly macrophages, in which the replication and differentiation of the agent from promastigote to amastigote occur [8]. The main symptoms of VL in humans are long-lasting fever, weakness, weight loss, anemia, hepatosplenomegaly, and a marked drop in the patient's blood platelet count [9]. VL detection can be performed by an immunological test, which aims to detect anti-_Leishmania_ antibodies; however, this modality presents an uncertain diagnosis due to different stages of the infection and the low reproducibility between different serological tests [10]. On the other hand, the gold standard for detecting VL is microscopy of material aspirated from the bone marrow to search for amastigotes of the parasite in all slide fields [11]. The microscopy examinations generate a significant volume of images, which requires considerable time for proper detection and annotation. Specialists carry out this process and it is subject to errors, depending on the operator's skill and the quality of the collected sample [12]. Despite the established conventions in the annotation process, many images are complex, resulting in miscount. Therefore, it is crucial to have an efficient diagnostic method to assist specialists in disease management [13]. The use of computational methods for analyzing medical images has several advantages over the traditional method of manual analysis performed by health professionals [14]. These advantages include faster analysis, reduced variability between results obtained by different specialists, the possibility of automated processing of large volumes of data, and the detection of subtle patterns that may go unnoticed by the human eye [14]. In addition, computational methods can allow more accurate decision-making and help health professionals in the early detection and treatment of diseases [15]. In order to alleviate repetitive work, it is pertinent to use machine learning techniques for processing medical images capable of diagnosing diseases [16].