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.
CITATION STYLE
Sánchez, L., Quintian, H., Alfonso-Cendón, J., Pérez, H., & Corchado, E. (2017). Use of support vector machines and neural networks to assess boar sperm viability. In Advances in Intelligent Systems and Computing (Vol. 527, pp. 13–19). Springer Verlag. https://doi.org/10.1007/978-3-319-47364-2_2
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