## 2 Background
These coefficients are based on the Kappa coefficient, which is another statistical method that provides a probability of success [16]. A good result of these coefficients is a number that is greater than 0.70, and this means that the segmentation method used supports image processing for prediction making. In other methods, Reference [17] uses image analysis and fractal dimensions to detect tumors in computed tomography (CT) scan images with high contrast, where the image preprocessing contrast of the cut images was improved by converting the values in the image intensity, using the histogram equalization to increase the accuracy of the tumor diagnosis. The image noise was reduced by the use of median filtering; finally, border detection was carried out by using the border function developed in MATLAB. This method has better performance and provides more acceptable responses than statistical algorithms. In Reference [18], the authors describe the development and implementation of feature selection for content-based image retrieval (CBIR) through a system that automatically extracts features from images using color, texture, and shape in order to use feature selection by means of a genetic algorithm that searches for the best feature-use feature selection. The results of this study conclude that the CBIR system is more efficient, and that it performs better when using feature selection based on a genetic algorithm, because it reduces the time for retrieval and also increases the retrieval precision. On the other hand, the authors in Reference [19] presented a study that proposed a 14-layer convolutional neural network, combined with three advanced techniques: batch normalization, dropout, and stochastic pooling in order to carry out multiple sclerosis identification. The results of this study concluded that with this model they obtained an accuracy of 98.77 \(\pm\) 0.39%. Results were compared with CNN when using maximum pooling and average pooling; the comparison showed that stochastic pooling gave a better performance than the other two pooling methods. Furthermore, in Reference [20], the researchers proposed to reconstruct objects based on incomplete images and some information on the 3D object using active and passive methods to reconstruct high-resolution items. Additionally, mathematics is essential for image processing; in Reference [21], the researchers used an arithmetic method to find the right and more trustworthy solution for quantization tables, which are tables for image compression. In a similar field, the use of statistical analysis for algorithm optimization is a good way to optimize models; in Reference [22], this type of analysis was used to enhance a cognitive model for a particle swarm based on vorticity, or the tendency of something to rotate. On basis of this panorama, the present study intends to carry out the recognition of images by the means of deep learning techniques, such as convolutional neural networks and image processing by color-space segmentation, with the purpose of determining the performance variation of a convolutional neural network, applying color-space segmentation to one of the test datasets. In the next section, the selected study scenario is presented.