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2017 | OriginalPaper | Chapter

Use of Support Vector Machines and Neural Networks to Assess Boar Sperm Viability

Authors : Lidia Sánchez, Héctor Quintian, Javier Alfonso-Cendón, Hilde Pérez, Emilio Corchado

Published in: International Joint Conference SOCO’16-CISIS’16-ICEUTE’16

Publisher: Springer International Publishing

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Abstract

This paper employs well-known techniques as Support Vector Machines and Neural Networks in order to classify images of boar sperm cells. Acrosome integrity gives information about if a sperm cell is able to fertilize an oocyte. If the acrosome is intact, the fertilization is possible. Otherwise, if a sperm cell has already reacted and has lost its acrosome or even if it is going through the capacitation process, such sperm cell has lost its capability to fertilize. Using a set of descriptors already proposed to describe the acrosome state of a boar sperm cell image, two different classifiers are considered. Results show the classification accuracy improves previous results.

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Metadata
Title
Use of Support Vector Machines and Neural Networks to Assess Boar Sperm Viability
Authors
Lidia Sánchez
Héctor Quintian
Javier Alfonso-Cendón
Hilde Pérez
Emilio Corchado
Copyright Year
2017
DOI
https://doi.org/10.1007/978-3-319-47364-2_2

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