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Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/73085
Title: 
Automatic classification of fish germ cells through optimum-path forest
Author(s): 
Institution: 
  • Universidade Estadual Paulista (UNESP)
  • Southwest Paulista College
ISSN: 
1557-170X
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. © 2011 IEEE.
Issue Date: 
26-Dec-2011
Citation: 
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, p. 5084-5087.
Time Duration: 
5084-5087
Keywords: 
  • Automatic classification
  • Germ cells
  • Machine learning techniques
  • Recognition accuracy
  • Supervised pattern recognition
  • Pattern recognition
  • Cells
Source: 
http://dx.doi.org/10.1109/IEMBS.2011.6091259
URI: 
Access Rights: 
Acesso restrito
Type: 
outro
Source:
http://repositorio.unesp.br/handle/11449/73085
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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