## 5 Discussion  
The results in Section 4.1 demonstrate that the overall accuracy of the classifiers was 96.0% for the ResNet18 classifier, 96.4% for the ResNet50 classifier, and 96.3% for the ResNet101 classifier. The accuracy of the ResNet50 classifier was 96.4%, and that \begin{table}
\begin{tabular}{c c} \hline \hline
**CNN a** & **Average Accuracy [\%]** \\ \hline ResNet18 & 95.8 \\ ResNet50 & 96.3 \\ ResNet101 & 96.1 \\ \hline \end{tabular} \({}^{\text{a}}\) Convolutional neural network.  
\end{table} 
Table 10: Average accuracy for each mammography image in each CNN. \begin{table}
\begin{tabular}{c c} \hline \hline
**CNN a** & **AUC b** \\ \hline ResNet18 & 0.956 \\ ResNet50 & 0.963 \\ ResNet101 & 0.961 \\ \hline \end{tabular} \({}^{\text{a}}\) Convolutional neural network; b area under the curve.  
\end{table} 
Table 8: AUC of classifiers in each CNN. \begin{table}
\begin{tabular}{c c} \hline \hline
**CNN a** & **Time for Classification [s]** \\ \hline ResNet18 & 0.072 \\ ResNet50 & 0.136 \\ ResNet101 & 0.203 \\ \hline \end{tabular} \({}^{\text{a}}\) Convolutional neural network.  
\end{table} 
Table 9: Inference time by classifier for each CNN. Figure 2: Accuracy of one mammography image at each CNN. of the ResNet101 classifier was 96.3%. Alternatively, in a previous study of two-class classification of the presence or absence of calcification, the authors reported that the most accurate classifier was 96% [33]. The findings obtained in the current study are comparable with those of the previous research. However, the ResNet18, ResNet50, and ResNet101 classifiers had sensitivities of 73.4%, 76.2%, and 75.6%, respectively, whereas the highest sensitivity in the previous study [33] was 98%. Compared with the results of the prior report, the sensitivity obtained in our study was relatively lower. One of the reasons for this is that in previous studies, patches were extracted from mammographic images in such a way that there was slight overlap in the top, bottom, left, and right sides of the image, which was reported to reduce the FN rate (1--sensitivity) [33; 34]. A recent study [32] using the ResNet network showed that deep learning tools aid radiologists in mammogram-based breast cancer diagnosis. The study addressed class imbalance issues in training data by testing techniques like class weighting, over-sampling, under-sampling, and synthetic lesion generation. The results indicated a bias toward the majority class due to class imbalance, which was partially mitigated by standard techniques but did not significantly improve AUC-ROC. Synthetic lesion generation emerged as a superior method, particularly for out-of-distribution test sets. The morphology and distribution of the calcifications in the breast vary. Obvious benign calcifications include skin and vascular calcification, fibroadenoma calcification, calcification associated with ductal dilatation, round calcification, central translucent calcification, calcareous calcification, calcareous milk calcification, suture calcification, and heterotopic calcification. The morphology of calcification, which must be distinguished between good and bad, includes microcircular, pale and indistinct, pleomorphic, fine-linear and fine-branched, diffuse, scattered, regional, clustered, linear, and zonal distribution. Because of the variations in the morphology and distribution of calcifications, we assume that the diversity of features was not captured in this study, resulting in a low sensitivity. In addition, FN patches had a smaller area of calcification as compared with TP patches. Most coarse calcifications were correctly classified as calcified. Other calcifications were often judged as calcified when a patch contained many calcifications or slightly larger calcifications, but they were incorrectly classified as no calcified when a patch contained only a few small calcifications, such as one or two small calcifications (i.e., when the area of calcification in the patch was small).