Automatic classification of fish germ cells through optimum-path forest

• Autor(a) principal: Papa, João Paulo [UNESP]
• Data de Publicação: 2011
• Outros Autores: Gutierrez, Mario E. M. [UNESP], Nakamura, Rodrigo Y. M. [UNESP], Papa, Luciene P., Vicentini, Irene Bastos Franceschini [UNESP], Vicentini, Carlos Alberto [UNESP]
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/IEMBS.2011.6091259; http://hdl.handle.net/11449/73085
• topic: Automatic classification; Germ cells; Machine learning techniques; Recognition accuracy; Supervised pattern recognition; Pattern recognition; Cells

The spermatogenesis is crucial to the species reproduction, and its monitoring may shed light over some important information of such process. Thus, the germ cells quantification can provide useful tools to improve the reproduction cycle. In this paper, we present the first work that address this problem in fishes with machine learning techniques. We show here how to obtain high recognition accuracies in order to identify fish germ cells with several state-of-the-art supervised pattern recognition techniques. © 2011 IEEE.