## 5 Results, Analysis, and Discussions  
According to the Table 3, taking as a guide Reference [27], and the results and performances based on individual, average, and overall classification model accuracies, the 10 best models were selected  
Figure 8: ROC Curve: DataSetFilter.  
Figure 7: Receiver operating characteristic (ROC) Curve: DataSetOriginal.  
by Random Search, and variations in the coefficients of loss, accuracy, and mean squared error that were input to their validation through dataSetOriginal and dataSetFilter, are presented. An average variation of 0.3945 in the function of loss, and an average variation of 0.1483 in the function accuracy, and a variation of 0.1118 in the function mean squared error, were obtained.  
From the evaluation made for the CNN model, with dataSetOriginal and dataSetFilter, the following aspects were analyzed: loss function, hit metric, confusion matrix, and ROC curve, and the following results were obtained:  
When evaluating the CNN with dataSetOriginal, 72% was obtained as a function of loss, while with dataSetFilter, 36% was obtained; therefore, a 36% decrease was obtained. On the other hand, through dataSetOriginal, a metric of hits was obtained with a percentage of 70%; while using dataSetFilter, 87% correct answers were obtained, presenting an increase of 17% between the two test datasets.  
Regarding the confusion matrix, the percentage of correct answers when using dataSetOriginal was 61%, while with dataSetFilter, a percentage of 87.7% was obtained, presenting an increase of 26.7%. In addition, in Table 7, the specific analysis for each of the geometric figures is presented.  
An ROC curve is a graph that shows the performance of a classification model in all of the classification thresholds [28]. When analyzing an ROC curve, the determining parameter is the area under the curve (AUC); therefore, it is the factor that will be analyzed next.  
Figure 8 represents the ROC curve when using dataSetOriginal, where, according to the AUC, the minimum average is 0.87 and the maximum average is 0.91, obtaining an average yield of 0.89; on the other hand, Figure 9 presents the curve of the ROC when using dataSetFilter, where the minimum average of the AUC is 0.97 and the maximum average is 0.98, obtaining an average yield of 0.975, and presenting an increase of 0.085 yield. Through a specific analysis, Table 8 presents the data and variations of the AUC of each of the classes by using the two test datasets.  
From Table 9, the performance in each of the classes presented variations with respect to the other test datasets, but the AUC value for image recognition, which previously had segmentation by color-space, was higher.  
In addition, we agree with results shown in Reference [29], since it states that for a prediction model to be considered optimal, the curve described must be convex, and in this case in Figure 9, taking the Macro curve as a reference, we can show that convexity occurs, since when making a region  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **Figure** & **Hits (\%)** & **Failures (\%)** \\ \hline \multirow{3}{*}{**dataSetOriginal**} & Circle & 80 & 20 \\  & Square & 43.3 & 56.3 \\  & Triangle & 60 & 40 \\  & Circle & 93.3 & 6.7 \\ \multirow{3}{*}{**dataSetFilter**} & Square & 73.3 & 26.3 \\  & Triangle & 96.7 & 3.3 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Confusion matrix analysis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **Class** & **Figure** & **AUC** \\ \hline \multirow{3}{*}{**dataSetTOriginal**} & 0 & Circle & 0.87 \\  & 1 & Square & 0.93 \\  & 2 & Triangle & 0.92 \\  & 0 & Circle & 0.98 \\ \multirow{3}{*}{**dataSetFilter**} & 1 & Square & 0.94 \\  & 2 & Triangle & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 9: ROC curve analysis.  
between this curve and the diagonal, putting any two points in the region, and passing a line through them, the curve is entirely within this region.