| Full text | |
| Author(s): |
Papa, Joao P.
;
Gutierrez, Mario E. M.
;
Nakamura, Rodrigo Y. M.
;
Papa, Luciene P.
;
Vicentini, Irene B. F.
;
Vicentini, Carlos A.
;
IEEE
Total Authors: 7
|
| Document type: | Journal article |
| Source: | 2011 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC); v. N/A, p. 4-pg., 2011-01-01. |
| 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. (AU) | |
| FAPESP's process: | 09/16206-1 - New trends on optimum-path forest-based pattern recognition |
| Grantee: | João Paulo Papa |
| Support Opportunities: | Research Grants - Young Investigators Grants |