Computer Vision in Automatic Visceral Leishmaniasis Diagnosis: a Survey

Visceral Leishmaniasis (VL) is a neglected disease that affects 1 billion people in tropical and subtropical countries. In Brazil, VL causes about 3,500 cases/year. Although this disease is lethal when left untreated, the number of cases is increasing. Thus, it is necessary to study current and safety technologies for VL diagnosis, treatment, and control. Specialized laboratories carry out the LV diagnosis, and this step has great automation power through automatic methods based on computer vision to aid in diagnosis. This work aims to present state-of-the-art research on computer vision techniques to detect VL in humans and provide a theoretical basis for developing computational systems to aid in diagnosing VL. This work's contributions are finding the methodologies and algorithms used in VL automatic detection and listing the gaps in developing those systems. As a result, we find out the lack of image databases and the use of deep learning techniques is still scarce. We conclude that methodologies that use the segmentation procedure perform better in terms of accuracy and that it is possible to develop a CAD system to help diagnose VL in humans.