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Classification of sperm cells according to their chromosomic content using a neural network trained with a genetic algorithm

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4 Author(s)
Kuri-Morales, A.F. ; Departamento de Computacion, Inst. Technol. Autonomo de Mexico, Mexico City, Mexico ; Ortiz-Posadas, M.R. ; Zenteno, D. ; Penaloza, R.

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.

Published in:

Engineering in Medicine and Biology Society, 2003. Proceedings of the 25th Annual International Conference of the IEEE  (Volume:3 )

Date of Conference:

17-21 Sept. 2003