Resumen de Use of Support Vector Machines and Neural Networks to Assess Boar Sperm Viability
Lidia Sánchez González, Héctor Quintián Pardo, Javier Alfonso Cendón, Hilde Pérez García, Emilio Santiago Corchado Rodríguez
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.
