## 4 Results and Discussion
### Discussion
Table 9 shows that the number of patches with calcification learned by the classifier differs by about 8000 when the classification probability is 0.80 and when the classification probability is 1.00. Therefore, it can be considered that more calcification features were learned when the classification probability was 0.80. On the other hand, precision tended to be low when the classification probability was low and high when the classification probability was high. The reason for this is thought to be that when the classification probability is high, only patches with greater certainty are learned to be transferred as having calcification, and patches without calcification are less likely to be incorrectly transferred as having calcification. As mentioned earlier, patches with lower classification probability tended to be incorrectly transfer-learned as having calcifications, even when they did not contain calcifications. Therefore, the lower the classification probability, the lower the precision will be. In this study, the patches without calcification were extracted only for the number of calcification patches that satisfied the classification probability, ensuring that the number of calcification patches with calcification and without calcification that were learned by transfer was equal. However, we believe that directly extracting the same number of patches without calcifications as the patches with calcifications used for transfer learning would increase the diversity of patches without calcifications and decrease false positives. (For example, consider the case where a classifier is created by transfer learning patches that satisfy the classification probability of 0.80. In this study, 2563 patches without calcification, matching the number of patches with calcifications that satisfied the classification probability, were extracted from 27,526 patches without calcifications. The number of patches with and without calcifications was then augmented by a factor of 10 for transfer learning. However, what is mentioned here is that 25,630 patches were directly extracted from the 27,526 patches without calcifications that satisfied the classification probability). Next, we discuss the differences among TP, TN, FP, and FN patches by classification probability. For the FP patches, the patches with dense breast tissue tended to be classified as having calcifications, and the images with dense breast tissue and no calcifications were also incorrectly classified as having calcifications. For the TP patches, the lower the classification probability, the more likely it was to correctly classify patches with dense breast tissue and calcifications as having calcifications, leading to an improvement in recall and a reduction in missed calcifications within dense breasts. Therefore, while conventional supervised learning tends to miss calcifications within dense breasts, the semi-supervised approach in this study can detect calcifications with high accuracy, regardless of the breast density of the input images, contributing to the improvement of recall. As with all classifiers, these classifiers tended to incorrectly judge a patch as without calcification when the area of calcification in the patch was small. If a patch contained many calcifications, slightly larger calcifications, or clusters of small calcifications, it could be classified as calcified. However, if a patch contained only one or two small calcifications, it tended to be incorrectly judged as without calcification. Therefore, this study divided the images into 224 \(\times\) 224 pixel patches to fit the ResNet50 input size. However, if the patches were divided into smaller matrices and then enlarged to match the CNN input size by resizing, we believe that the area of calcification in the patches would increase, potentially reducing the number of false negative patches. Next, we compare our results with those of similar studies.