## 4 Conclusions  
Most of the works in the literature about chromosome classification are trained using Q-band image datasets. The two-stage classification model proposed in this work was trained with a dataset of images acquired with the more affordable and common G-band staining technique. The dataset composed of 24 images was provided by the CHT. This work was limited to the study of the best classifier based on the classification accuracy for the task of classifying G-band chromosome images to build a karyogram. Only the classifiers available through the Weka platform were tested. The ANN that was finally selected was used with its default configuration. Deep-learning techniques were not studied because they require manually annotating large amounts of data for training, and it is a very time consuming and expensive process. The set of karyotype images provided by the CHT is composed of only 24 images, hence only classical classification algorithms were tested. Execution time for each classifier was not studied, but results showed that between 5 and 7 s are needed to classify a whole set of segmented chromosomes, once the trained model was obtained. The MLP-ANNs have proven their effectiveness in modelling features that best describe a phenomena. In this work, the features extracted from the chromosome images are used to feed a MLP-ANN. Three sets of features were tested and the set best describing the chromosomes, in the aimed application context, was selected. This feature set is allows the ANN to distinguish between 24 different classes. The main contribution of this work is the proposal of a two-stage chromosome classification scheme. In the first stage, the chromosomes are classified into 3 wide classes. This output is used as the input of a second stage, where the pre-classified chromosomes are assigned to a 1 of the 24 available classes. This scheme improved the classification accuracy from 76% to 88% when using the Q-band database. The two stage chromosome classification scheme proposed in this work achieves results comparable with those found in the literature, even when a non-optimized MLP-ANN classifier was used. As a future work, the optimization of the network hyper-parameters parameters and topology will be explored. To objectively determine the most suited classification algorithm for the aimed application, a set of 51 algorithms was evaluated using a dataset different from the one used during the training stage. In this evaluation, the MLP-ANN obtained the best classification results, both with the multi-class classifiers and the binary ones. Using the Q-band dataset [26], the proposed classification scheme correctly classified 88% of the whole set, while using the G-band image dataset, it obtained an accuracy of 82%. It should be noted that the G-band image dataset is composed of only 24 karyotypes, while the Q-band is composed of 119 studies. It was observed that reducing the number of neurons in the hidden layer of the MLP-ANN reduced the training time without affecting the classification results. In fact, for the 3 tested binary classifiers, starting from 4 neurons, the classification accuracy was similar to that obtained when using the 66 default neurons. Finally, the classification scheme presented in this work was implemented into an application that allowed the cytogeneticist from the CHT to reduce the required time to generate a cytogenetic report from several hours to some minutes. The integration of an automatic system in all the processing phases: chromosome segmentation, chromosome classification and the automated diagnose of genetic diseases is desired. However, several issues must be solved to attain this state, such as the lack of a bigger set of labelled G-banding images. A bigger set of images would increase the classification accuracy of the existing models and could allow us to test of a more robust classification scheme based on deep learning techniques. Also, the integration of a fully automatic segmentation module would complete the proposed system.