## Materials and Methods
### Methods
#### 2.2.1 Outline of the Proposed Automatic Classification
The training and testing accuracies are reported, and they are used as evaluation metric for the next activity. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Set** & **Type** & **Outputs** & **Description** \\ \hline \(q_{1}\) & Multiclass & 24 & Classify chromosomes into 24 classes \\ \hline \(q_{2}\) & Multiclass & 3 & Classify chromosomes into 3 groups \\  & & & G1: Chromosomes from 1 to 7 \\  & & & G2: Chromosomes from 8 to 15 \\  & & & G3: Chromosomes from 16 to 24 \\ \hline \(q_{3}\) & Binary & 6 & 3 binary classifiers are included: \\  & & & BG1: Chromosomes belongs to G1 or not \\  & & & BG2: Chromosomes belongs to G2 or not \\  & & & BG3: Chromosomes belongs to G3 or not \\ \hline \(q_{4}\) & Multiclass & 24 & 3 multiclass classifiers are included: \\  & & & MG1: Chromosomes from 1 to 7 \\  & & & MG2: Chromosomes from 8 to 15 \\  & & & MG3: Chromosomes from 16 to 24 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Subset of architectures \(\{q_{1}\), \(q_{2}\), \(q_{3}\), \(q_{4}\}\subseteq Q\). \begin{table}
\begin{tabular}{l} \hline \hline
1 1B1 (Instance-based classifier) \\
2 KStar \\
3 Random Forest \\
4 Multilayer perceptron \\
5 SMO (Sequential Minimal Optimization) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Subset of classification algorithms \(\{a_{2}\}\subseteq A\). Once several models were generated (through activities 1 to 4), the best chromosome classification model, \(m_{n}\), is identified, based on the chromosome classification accuracy. Current \(m_{n}\) could represent, either the output of phase 1, or the final classification model, \(m\). Phase 2. Classifier Analysis  
In this phase, the algorithm \(a_{j}\) from the classification model \(m\) obtained in phase 1 is analyzed in order to find a relationship between the results and the configuration of this algorithm. This is an intermediate phase to integrate the selected classifier to the next phase. Phase 3. Application Development  
Here, a GUI is developed to allow the cytogenetist to use a semi-automatic tool to segment the chromosomes in G-band images. Then, the segmented chromosomes are used to build the G-band image database. This tool is also used to extract the chromosome features defined in the classification model, \(m\), that was obtained as a result of phase 1. Phase 4. Classifiers Training  
During phase 1, a classification model, \(m\), was obtained by training the algorithms \(a_{j}\) using the Q-band image database. In this phase, the algorithms \(a_{j}\) are trained again, following the classification architecture \(q_{k}\) obtained in phase 1, but this time using the features extracted from the G-band chromosome images. To validate each model the k-fold cross validation is used with a k = 10. Phase 5. Application Integration  
In this phase, an application consisting of 3 modules, named A, B and C, was developed. **Module A:**: This module includes a GUI and the segmentation related operations. It allows the user to generate the segmented chromosomes and arrange them in directories. **Module B:**: It comprises the automatic classification operations, including the development of a GUI that allows the user to: (i) Enter the segmented chromosomes obtained by the module A; (ii) Use the classification model \(m\), obtained in phase 4. Its output is composed of the classified chromosomes. **Module C:**: It is a GUI that generates a karyogram using the classified chromosomes obtained in module B. This karyogram is interactive, since the module allows the user to change the chromosome polarity and its membership class. In addition, this module generates a cytogenetic report in the format defined by the cytogeneticist.