Papa, João Paulo [UNESP]Gutierrez, Mario E. M. [UNESP]Nakamura, Rodrigo Y. M. [UNESP]Papa, Luciene P.Vicentini, Irene Bastos Franceschini [UNESP]Vicentini, Carlos Alberto [UNESP]2014-05-272014-05-272011-12-26Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, p. 5084-5087.1557-170Xhttp://hdl.handle.net/11449/73085The 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.5084-5087engAutomatic classificationGerm cellsMachine learning techniquesRecognition accuracySupervised pattern recognitionPattern recognitionCellsTrabalho apresentado em evento10.1109/IEMBS.2011.6091259WOS:000298810004007Acesso aberto2-s2.0-84055193445903918293274719495814680589219523150094336796923