The karyotyping step is essential in the genetic diagnosis process, since it allows the genetician to see and interpret patient’s chromosomes. Today, this step of karyotyping is a time-cost procedure, especially the part that consists in segmenting and classifying the chromosomes by pairs. This paper presents a compartive study of image classification of banded human chromosomes for automated karyotyping (AKS), by using classifier ensembles. The goal of this contribution is to propose and evaluate a solution to automate the karyotyping, from microscope images to the obtention of the classified chromosomes. For this purpose, we have evaluated several approaches based on classifier ensembles trying to find a solution that shows better trade-off between accuracy and computational cost.
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