Automatic classification of fish germ cells through optimum-path forest

dc.contributorUniversidade Estadual Paulista (Unesp)
dc.contributorSouthwest Paulista College
dc.date.accessioned2014-05-27T11:26:20Z
dc.date.accessioned2022-10-05T18:32:07Z
dc.date.available2014-05-27T11:26:20Z
dc.date.available2022-10-05T18:32:07Z
dc.date.created2014-05-27T11:26:20Z
dc.date.issued2011-12-26
dc.identifierProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, p. 5084-5087.
dc.identifier1557-170X
dc.identifierhttp://hdl.handle.net/11449/73085
dc.identifier10.1109/IEMBS.2011.6091259
dc.identifierWOS:000298810004007
dc.identifier2-s2.0-84055193445
dc.identifier9039182932747194
dc.identifier9581468058921952
dc.identifier3150094336796923
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3922104
dc.description.abstractThe 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.
dc.languageeng
dc.relationProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
dc.rightsAcesso aberto
dc.sourceScopus
dc.subjectAutomatic classification
dc.subjectGerm cells
dc.subjectMachine learning techniques
dc.subjectRecognition accuracy
dc.subjectSupervised pattern recognition
dc.subjectPattern recognition
dc.subjectCells
dc.titleAutomatic classification of fish germ cells through optimum-path forest
dc.typeTrabalho apresentado em evento


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