Use of Support Vector Machines and Neural Networks to Assess Boar Sperm Viability
Lídia Sánchez-González, Héctor Quintián, Javier Alfonso‐Cendón, Hilde Pérez, Emilio Corchado · Advances in intelligent systems and computing · 2016
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.