## 4 Results and Discussion
### Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Classification** \\ **Probability** \\ \end{tabular} } & \begin{tabular}{c} **Recall** \\ **(Improvement** \\ **Ratio)** \\ \end{tabular} & \begin{tabular}{c} **Precision** \\ **(Improvement** \\ **Ratio)** \\ \end{tabular} & \begin{tabular}{c} **Overall Accuracy** \\ **(Improvement** \\ **Ratio)** \\ \end{tabular} & \
\begin{tabular}{c} **AUC a** \\ **(Improvement** \\ **Ratio)** \\ \end{tabular} &
\begin{tabular}{c} **FI Score** \\ **(Improvement** \\ **Ratio)** \\ \end{tabular} \\ \hline (original ResNet50) & 0.778 & 0.751 & 0.974 & 0.969 & 0.765 \\  & 0.866 & 0.636 & 0.966 & 0.974 & 0.734 \\  & (111.26\%) & (84.71\%) & (99.16\%) & (100.53\%) & (95.95\%) \\
0.85 & 0.845 & 0.643 & 0.966 & 0.970 & 0.731 \\  & (108.61\%) & (85.61\%) & (99.19\%) & (100.18\%) & (95.55\%) \\
0.90 & 0.856 & 0.678 & 0.970 & 0.971 & 0.756 \\  & (109.93\%) & (90.19\%) & (99.59\%) & (100.28\%) & (98.92\%) \\
0.95 & 0.856 & 0.675 & 0.970 & 0.976 & 0.755 \\  & (109.93\%) & (89.82\%) & (99.57\%) & (100.77\%) & (98.69\%) \\
1.00 & 0.830 & 0.682 & 0.970 & 0.971 & 0.749 \\  & (106.62\%) & (90.81\%) & (99.57\%) & (100.28\%) & (97.94\%) \\ \hline \hline \end{tabular}  
* Area under the curve.  
\end{table} 
Table 10: Accuracy of each classifier and relative improvement from the initial classifier. breasts can be more accurately classified as present. The breast is composed of glandular tissue and fatty tissue, and based on the proportion of these components, breast density is classified into four categories: \"fatty\", \"scattered fibroglandular\", \"heterogeneously dense\", and \"extremely dense\". The latter two categories, \"heterogeneously dense\" and \"extremely dense,\" are referred to as dense breasts, where there is little fat and a higher proportion of glandular tissue, causing the images to appear white. This reduces the sensitivity of mammography and lowers the detection rate of lesions [43; 44]. Additionally, there is a deep relationship between breast density and breast cancer risk, with women who have dense breasts being at a higher risk of breast cancer compared to women with fatty breast tissue [45; 46]. Given these factors, detecting calcifications in dense breasts is important. Thus, the increase in recall and the reduction in the false negative rate, along with the improved accuracy in classifying patches with calcifications in dense breasts, demonstrate the usefulness of semi-supervised learning in calcification detection. Next, we discuss the differences in TP, TN, FP, and FN patches between the initial classifier and the classifier after transfer learning. First, compared to the initial classifier, the transfer learning of new data by semi-supervised learning tended to correctly classify the TP patches as having calcifications in areas with much mammary tissue, where the image was rendered white, that is, patches with little contrast between the calcification and the mammary gland. However, for the FP patches, compared to the initial classifier, after transfer learning, the classifier tended to incorrectly classify patches with more mammary tissue and no calcification where the image was rendered in white as having calcification. Thus, it is thought that transfer learning made it easier to classify patches with more mammary tissue and white images as having calcification. Next, we discuss the difference in recall and precision by classification probability. From Table 10, recall tended to be higher when the classification probability was low and lower when the classification probability was high. The reason for this is that more patches satisfy the classification probability when the classification probability is low, more patches with calcification are learned by transfer, more various calcification features are learned, and the diversity of calcification learned by the classifier is expanded.