  Leishmaniasis is considered a neglected disease that causes thousands of
deaths annually in some tropical and subtropical countries. There are various
techniques to diagnose leishmaniasis of which manual microscopy is considered
to be the gold standard. There is a need for the development of automatic
techniques that are able to detect parasites in a robust and unsupervised
manner. In this paper we present a procedure for automatizing the detection
process based on a deep learning approach. We train a U-net model that
successfully segments leismania parasites and classifies them into
promastigotes, amastigotes and adhered parasites.
