# Leishmaniasis Parasite Segmentation and Classification using Deep Learning

## TL;DR

This paper presents a deep learning-based method using U-net for automatic segmentation and classification of Leishmania parasites, aiming to improve diagnosis accuracy and efficiency over manual microscopy.

## Contribution

It introduces a novel deep learning approach for automatic parasite detection and classification, enhancing diagnostic processes for leishmaniasis.

## Key findings

- Successful segmentation of parasites using U-net
- Accurate classification into promastigotes, amastigotes, and adhered parasites
- Potential for automated diagnosis in clinical settings

## Abstract

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.