# Leishmaniasis Parasite Segmentation and Classification using Deep Learning

arXiv: https://arxiv.org/abs/1812.11586 · HTML: https://mlsanity.com/paper/1812.11586/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2018.

## Built on by (most cited first, 1 of 1)

- [PlasmoID: A dataset for Indonesian malaria parasite detection and segmentation in thin blood smear](https://mlsanity.com/paper/2211.15105/) (2022)

## Abstract (arXiv)

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.