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

• Lidia Sánchez ^[1] ; Héctor Quintian ^[2] ; Javier Alfonso-Cendón ^[1] ; Hilde Pérez ^[1] ; Emilio Corchado ^[2]
  1. [1] Universidad de León

     Universidad de León

     León, España

     
  2. [2] Universidad de Salamanca

     Universidad de Salamanca

     Salamanca, España

     
• Localización: International Joint Conference SOCO’16-CISIS’16-ICEUTE’16: San Sebastián, Spain, October 19th-21st, 2016 Proceedings / coord. por Manuel Graña Romay, José Manuel López Guede, Oier Etxaniz, Álvaro Herrero Cosío, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 2017, ISBN 978-3-319-47364-2, págs. 13-19
• Idioma: inglés
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• Resumen

  • 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.