# Classification of Breast Cancer in Mammograms with Deep Learning Adding a Fifth Class
## 4 Results  
We evaluated the classification efficiency of the CNNs, with various performance metrics based on the classification performed on the set of tests and its confusion matrix. Table 4 shows the types of training and their results on the test set; the table is organized by the best AUC and shows the accuracy, precision, sensitivity (recall), specificity, and the F1 Score for each classifier. The CNN with the best performance and highest metrics was the GoogLeNet network trained with the balanced database with five classes that applies the normal tissue classification. In this training, the values obtained show outstanding performance, high efficiency, and the best percentages for precision, recall, and F1 score. The training and tests of GoogLeNet with the balanced database of five classes used 11,680 regions, with 70% for training and 30% for tests, corresponding to 8175 and 3505 regions, respectively. In Figure 4, the training progress shows that as the iterations and training epochs increase, the accuracy also increases, and the error decreases. It can also be seen that the stoppage programmed in training to 99.5% accuracy performed its function to avoid overfitting, stopping the training in the middle of the 20 th epoch at the 20,054 iterations from 20,420 with an approximate time of 3 h of training. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**CNN** & **Classes** & **Database** & **F1 Score (\%)** & **AUC (\%)** & **Accuracy (\%)** & **Precision (\%)** & **Recall/Sensitivity** & **Specificity (\%)** \\ \hline GoogLeNet & 5 & Balanced & 91.92 & 99.29 & 91.70 & 92.15 & 91.70 & 97.66 \\ Vgg19 & 5 & Balanced & 91.18 & 99.50 & 91.16 & 91.21 & 91.16 & 97.66 \\ GoogLeNet & 4 & Balanced & 90.85 & 99.14 & 90.76 & 90.95 & 90.76 & 96.92 \\ AlexNet & 5 & Balanced & 88.27 & 98.60 & 87.87 & 88.67 & 87.87 & 96.58 \\ GoogLeNet & 5 & Unbalanced & 88.20 & 99.17 & 91.97 & 88.57 & 87.83 & 97.60 \\ ResNet50 & 4 & Balanced & 86.78 & 98.00 & 86.73 & 86.82 & 86.73 & 95.58 \\ AlexNet & 4 & Balanced & 86.32 & 97.82 & 86.31 & 86.33 & 86.31 & 95.44 \\ ResNet50 & 5 & Balanced & 85.81 & 98.53 & 85.73 & 85.89 & 85.73 & 96.13 \\ GoogLeNet & 4 & Unbalanced & 84.77 & 97.65 & 85.85 & 84.15 & 85.40 & 95.39 \\ AlexNet & 5 & Unbalanced & 83.18 & 98.09 & 88.66 & 82.97 & 83.40 & 96.52 \\ AlexNet & 4 & Unbalanced & 81.77 & 96.72 & 83.25 & 81.79 & 81.76 & 94.41 \\ ResNet50 & 5 & Unbalanced & 81.12 & 98.06 & 87.12 & 81.74 & 80.52 & 95.63 \\ ResNet50 & 4 & Unbalanced & 79.51 & 96.35 & 81.26 & 79.98 & 79.05 & 93.73 \\ Vgg19 & 4 & Balanced & 73.42 & 99.16 & 91.76 & 73.44 & 73.41 & 77.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance metrics for every pre-trained CNN. Table 5 shows the classifier's results comparing the current regions concerning the classifier's predictions with the GoogLeNet network with five categories and the percentages of each prediction resulting in the confusion matrix. Finally, to measure the efficiency of the classifiers generated by the CNN through transfer learning and fine-tuning, the receiver operating characteristic (ROC) curves were performed, measuring the AUC of all the classifiers. Figure 5 shows the \(x\) and \(y\) coordinates of the optimal ROC operating points for each type of findings and their respective AUCs  
Figure 4: Training progress of the CNN GoogLeNet trained with five categories and a balanced database.