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Leishmaniasis Parasite Segmentation and Classification using Deep Learning
lib:f3d945c08170e71a (v1.0.0)
Authors: Marc Górriz,Albert Aparicio,Berta Raventós,Verónica Vilaplana,Elisa Sayrol,Daniel López-Codina
ArXiv: 1812.11586
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1812.11586v1
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.
ArXiv: 1812.11586
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1812.11586v1
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.
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