title: Automatic classification of fish germ cells through optimum-path forest.
abstract: 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.
authors.affiliation: Department of Computing, UNESP - Univ Estadual Paulista, Bauru, Brazil. papa@fc.unesp.br
authors.forename: João P
authors.initials: JP
authors.lastname: Papa
authors.forename: Mario E M
authors.initials: ME
authors.lastname: Gutierrez
authors.forename: Rodrigo Y M
authors.initials: RY
authors.lastname: Nakamura
authors.forename: Luciene P
authors.initials: LP
authors.lastname: Papa
authors.forename: Irene B F
authors.initials: IB
authors.lastname: Vicentini
authors.forename: Carlos A
authors.initials: CA
authors.lastname: Vicentini
mesh_terms: D000818:Animals; D001185:Artificial Intelligence; D005399:Fishes / Q000033:anatomy & histology*; D005854:Germ Cells / Q000145:classification* / Q000166:cytology*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity