###### Abstract  
This work proposes the evaluation of a set of algorithms of machine learning and the selection of the most appropriate one for the classification of segmented chromosomes images acquired using the Giemsa staining technique (G-banding). The evaluation and selection of the best classification algorithms was carried out over a dataset of 119 Q-banding chromosomes images, and the obtained results were then applied to a dataset of 24 G-band chromosomes images, manually classified by an expert of the Laboratory of Cytogenetic of the Children's Hospital of Tamaulipas. The results of evaluation of 51 classifiers yielded that the best classification accuracy for the selected features was obtained by a backpropagation neural network. One of the main contributions of this study is the proposal of a two-stage classification scheme based on the best classifier found by the initial evaluation. In stage 1, chromosome images are classified into three major groups. In stage 2, the output of phase 1 is used as the input of a multiclass classifier. Using this scheme, 82% of the IGB bank samples and 88% of the samples of a bank of images obtained with a Q-band available in the literature consisting of 119 chromosome studies were successfully classified. The proposed work is a part of an desktop application that allows cytogeneticist to automatically generate cytogenetic reports. machine learning; karyotype; image processing 2020  
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Talax  
of machine learning based techniques for PD diagnosis based on dysphonia symptoms is reported. Even, the proposed system is focused on the classification nor the automatic diagnosis of genetic diseases, this could be considered as the following step in the proposed system. Once an image of chromosomes is obtained, they can be classified by an expert. Humans, in normal conditions have 46 chromosomes or 23 pairs. The classification is done according to the chromosome size, in descending order, including pairs from 1 to 22, taking the sexual chromosomes as the pair number 23. Chromosomes from the pair 23 can be classified as _XX_ for women or _XY_ for men. In summary, a human has 24 types of chromosomes. The classification of chromosomes by size and type is called _karyogram_. Figure 1 shows an image of cell in metaphase and the manual karyotype labeling performed by an expert cytogeneticist. Figure 2 shows the corresponding karyogram to the image in the Figure 1.  
Manual karyogram construction is a complex task demanding time and expertise. Nowadays, several efforts have been done to create automatic systems for dealing with computer-based karyotyping [2; 6; 7; 8; 9; 10; 11; 12; 13]. Several studies implement techniques from machine learning such as Support Vector Machines [7; 8; 14; 15; 16], Nearest Neighbor Algorithms [17; 18], Wavelets [19], Bayesian  
Figure 1: Manual karyotype labeling performed by an expert cytogeneticist over an image obtained with the G-banding technique. Figure 2: Obtained Karyogram from the classified chromosomes (karyotype) in Figure 1.  
techniques [20; 21] and mainly, Artificial Neural Networks [19; 22; 23; 24; 25]. Nevertheless, the automatic computer image-based karyotyping is an open research topic. One of the main problems arises when the image contains overlapping or touching chromosomes, because each chromosome must be cut individually to present the karyogram. Another important aspect to segment and classify the chromosomes is the applied staining technique to acquire the microscopic image. The most common staining techniques are G-band, C-band, R-band and Q-band, named upon the used stain in the cellular culture [2]. Q-banding is the first technique that was used in chromosome studies. There exists at least one public image database that has been used to develop computational systems to automatically build karyograms. One of these database was made available in Reference [26], and is composed of 119 chromosome studies, including their karyotype.