DocumentCode :
406814
Title :
Classification of sperm cells according to their chromosomic content using a neural network trained with a genetic algorithm
Author :
Kuri-Morales, A.F. ; Ortiz-Posadas, M.R. ; Zenteno, D. ; Penaloza, R.
Author_Institution :
Departamento de Computacion, Inst. Technol. Autonomo de Mexico, Mexico City, Mexico
Volume :
3
fYear :
2003
fDate :
17-21 Sept. 2003
Firstpage :
2253
Abstract :
A priori determination of the sex of a human individual before gestation is a desirable goal in some cases. To achieve this, it is necessary to perform the separation of sperm cells containing either X or Y chromosomes. As is well known, male sex depends on the presence of chromosome Y. Once this separation is achieved in principle, we require to determine, with a high degree of accuracy, whether the sperm cells of interest contain the desired X or Y chromosomes. If we are able to obtain certain simple measurements regarding the sperm cells under consideration we will be able to control the fertilization process reliably. In this paper we report a method which allows for non-invasive verification of the characteristics of the separated sperm. We determined a set of easily measurable characteristics. From a sample drawn from previously cropped sperm we trained a neural network with a genetic algorithm. The trained network was able to perform a posteriori classification with an error much smaller than 1%. This percentage of efficiency is better than the ones reported in centers of assisted fecundation.
Keywords :
cellular biophysics; genetic algorithms; learning (artificial intelligence); multilayer perceptrons; pattern classification; a posteriori classification; chromosomes; genetic algorithm; neural network; noninvasive verification; sperm cells; Biological cells; Cells (biology); Computer networks; Genetic algorithms; Humans; In vitro fertilization; Multilayer perceptrons; Neural networks; Process control; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, 2003. Proceedings of the 25th Annual International Conference of the IEEE
ISSN :
1094-687X
Print_ISBN :
0-7803-7789-3
Type :
conf
DOI :
10.1109/IEMBS.2003.1280246
Filename :
1280246
Link To Document :
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