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.
CITATION STYLE
Górriz, M., Aparicio, A., Raventós, B., Vilaplana, V., Sayrol, E., & López-Codina, D. (2018). Leishmaniasis parasite segmentation and classification using deep learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10945 LNCS, pp. 53–62). Springer Verlag. https://doi.org/10.1007/978-3-319-94544-6_6
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