## 4 Results and Discussion
### Discussion
Since no study used semi-supervised learning to classify the presence or absence of calcification in mammograms, we compare our results with similar studies [29] that use semi-supervised learning for pixel-based classification for tumor segmentation in mammogram images. Comparing \"Bayes\" as a classifier for supervised learning and \"Co-training\" as a classifier for semi-supervised learning in similar studies, semi-supervised learning has higher accuracy and sensitivity, or recall, and lower positive predictive accuracy, or precision. As in similar studies, semi-supervised learning increased the recall and decreased the precision in this study. On the other hand, the overall accuracy of this study was comparable to that of the initial classifier, which was different from similar studies. The difference can be attributed to the fact that this study used a self-training method in semi-supervised learning, whereas similar studies used a co-training method in semi-supervised learning. In self-training, a classifier is created by learning labeled data, classifying unlabeled data with the created classifier, and updating the classifier by transferring the classified data with high classification probability to the original classifier as labeled data. However, this method incorrectly transfers learning when unlabeled data are classified differently from the actual data. In order to reduce such errors as much as possible, there is a method called co-training. In this method, the labeled data are first divided into two parts, each of which is trained to create two classifiers. Next, unlabeled data are classified using each classifier, and the classified data with a high classification probability are transferred to a different classifier as labeled data, aiming for better learning by creating two different classifiers and alternately interpolating the information held by the two classifiers [47]. We believe that the difference in the methods used can be cited as the reason for the difference in whether the accuracy was the same or increased. A limitation of this study is that Asians tend to have more dense breasts than Westerners [48], which may affect the accuracy when used for mammographic images of Asian women. Therefore, in the future, it will be necessary to use a dataset of mammographic images of Asian women or to mix the data used in this study with mammographic images of Asian women. In addition, although this study only classified the presence or absence of calcification, calcification can be either benign or malignant [8; 10], and benign or malignant classification is also necessary for the diagnosis of breast cancer. In this study, ResNet, a common network model in the field of medical image classification [16], was used for classification, but the accuracy may change by changing the network model used. Additionally, one of the state-of-the-art (SOTA) models in image classification is OmniVec [49], but no studies have been found that use it for medical imaging. In a study published in 2024 [50] that classified mammographic images as normal, benign, or malignant, the images were input into the CNN without patch division. The approach of dividing mammographic images into patches and inputting them into the CNN, as adopted in this study, is a novel perspective. This study applied semi-supervised learning to classify the presence or absence of calcifications in mammograms. Although semi-supervised learning has been validated in various fields of medical imaging, supervised learning remains predominant in mammography, and there are few studies applying semi-supervised learning to this field. No studies have been found that apply semi-supervised learning to classify the presence or absence of calcifications in mammograms. Furthermore, while this study examined the differences in accuracy due to classification probability thresholds in semi-supervised learning, no prior studies have verified the differences in accuracy for classifying the presence or absence of calcifications based on varying classification probabilities.