# Breast Density Transformations Using CycleGANs for Revealing Undetected Findings in Mammograms
## 3 Experimental Results  
To evaluate the performance of the proposed CAD system for each of the previously presented mammographic databases, we have used the precision, recall, accuracy and F1-score metrics as follows:  
\[\text{Precision}=\frac{\text{True Positive}}{\text{True Positive}+\text{ False Positive}}\, \tag{1}\]  
Figure 10: Illustration of ACR heatmaps before and after CycleGAN transformations. ACR density sensitivity remains almost the same. \[\text{Recall}=\frac{\text{True Positive}}{\text{True Positive}+\text{False Negative}}, \tag{2}\]  
\[\text{F1}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}},\] \[\text{Accuracy}=\frac{\text{True Positive}+\text{True Negative}}{ \text{Total}}\, \tag{3}\]  
True positive (TP) represents the number of positive cases that have been correctly classified as positive. True negative (TN) is the number of negative classes that have been correctly classified as negative. False positive (FP) represents the number of negative classes that have been misclassified as the positive class. False negative (FN) represents the number of positive classes that have been misclassified as negative. Typically for each experiment, a confusion matrix was also generated reporting the following cases. The VinDR-Mammo dataset contains 20,000 total images, of which 988 (4.94%) are malignant, and 5606 (28.03%) are benign. The ACR density distribution is 0.5%, 9.54%, 76.46%, and 13.5% for the four ACR classes, respectively. To compare the overall improvement of the proposed CAD system, all ROI patches are classified before and after changing their density to ACR-A using the CycleGAN network. The performance of the CNN model based on VGG16 before and after utilizing the CycleGAN transformations is depicted in Figure 11, which shows a significant improvement in the metrics. The recognition results on the left of each dotted line correspond to the initial CNN performance, while the results on the right correspond to the performance after the application of the CycleGAN density transformations. The overall accuracy is dramatically increased from 85% to 91%. The SuReMaPP dataset contains 343 images, with 0 (0%) malignant and 132 (38.48%) benign cases. The results for the SuReMaPP dataset are shown in Figure 12. The overall accuracy is further improved from 96% to 98%. Figure 11: CNN performance before and after CycleGAN transformations—VinDR—Mammographic Database. The MIAS dataset contains 322 total images, with 54 (16.77%) malignant and 69 (21.43%) benign cases. The results for the MIAS dataset are shown in Figure 13. The overall accuracy is increased from 96% to 97%. The DDSM (digital database for screening mammography) dataset contains 10,480 images, with 1936 (18.7%) malignant and 2628 (25.4%) benign cases. The results for the DDSM dataset are shown in Figure 14. The overall accuracy is dramatically improved from 67% to 79%. Figure 12: CNN performance before and after CycleGAN transformations—SuReMaPP—Mammographic Database. Figure 10:0 CNN performance before and after CycleGAN transformations—MIAS—Mammographic Database. For comparison reasons, we report the proposed system's performance when using the INbreast dataset, which was used in all the relevant CNN/GAN-based topologies for training. The INbreast dataset contains 410 images, with 100 (24.39%) malignant, 243 (59.27%) benign, and 67 (16.34%) normal cases. The percentage of images in each ACR category is 36%, 35%, 22%, and 7%, respectively. In Table 1, we present the evaluation of the VGG16 classification model (normal-benign-malignant)  
For the incorrectly classified ROI patches, after exploiting the CycleGAN model to transform their density to ACR-A class, the classification results are shown in Figure 10:1