Leishmaniasis Parasite Segmentation and Classification using Deep Learning
This work addresses the need for robust, unsupervised diagnostic tools for Leishmaniasis, a neglected disease causing thousands of deaths annually, though it appears incremental as it applies an existing method to a new medical imaging task.
The paper tackled the problem of automating the detection of Leishmaniasis parasites by developing a deep learning-based procedure using a U-net model, which successfully segmented and classified parasites into promastigotes, amastigotes, and adhered parasites.
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.