## 5 Discussion
In addition, FN patches were often white than TP patches, especially when the entire patch was white and each patch contained only a few small calcifications (i.e., one or two). Conversely, FP patches included some patches with white patches. In addition, as with the FN patches, patches that were entirely white were often incorrectly classified as having calcification. These results suggest that calcification is often misjudged because it is difficult to distinguish between calcification and noncalcification when patches are whitish because calcification is depicted with high brightness. Therefore, we believe that the use of thresholding can improve the accuracy of the classifier by reducing the number of FN and FP patches via the extraction of areas of particularly high brightness. With regard to the point that a small area of calcification in a patch is often incorrectly classified as no calcification, in this study, we divided the mammography image into patches of 224 \(\times\) 224 pixels to create the input image size for ResNet; however, it is possible to divide the mammography image into patches of a smaller matrix size and to use the generated patches as the input image size for ResNet. However, if the patches are divided into a smaller matrix size and the generated patches are enlarged to 224 \(\times\) 224 pixels by resizing, the area of calcification in the patches will be larger and the accuracy may be improved. We will next discuss the AUC of the created classifiers. The AUC of the classifiers created by all CNNs was greater than 0.95, a value that is considered to be very accurate and excellent. Regarding the processing time for classifying the presence or absence of calcification for the created classifiers, ResNet18, ResNet50, and ResNet101 took the shortest processing times, in that order. This is consistent with the fact that deeper CNN layers require more processing time to extract more advanced and complex features. The classifier trained on ResNet50, which had the highest accuracy and AUC, took 0.136 s to classify the patches generated from a single mammogram image according to the presence or absence of calcification, which means that seven mammogram images can be processed per second. Next, we discuss the accuracy of the system for one mammogram at a time. The average accuracy for each mammogram was 95.3%. The interior of the breast is composed mainly of mammary glands and fat and is classified into four categories in accordance with the proportion of these components: fatty, scattered mammary, heterogeneous hyperintense, and extremely hyperintense. The percentage of mammary tissue is larger in the latter category. The two types of tissue, heterogeneous and extremely dense, are referred to as \"dense breasts\". Densely concentrated breast has less fat and more mammary tissue, resulting in a whiter image and a lower rate of lesion detection [35]. This makes it difficult to distinguish between calcification and noncalcification and is thought to reduce the accuracy of classifying the presence or absence of calcification. Because microcalcifications appear as localized areas of high brightness, the use of thresholding is expected to increase the accuracy of the classifier as a whole as well as the accuracy of each mammographic image. The specificity of microcalcification detection in CAD, which has been conventionally used as a reading aid, is 45%, and the FP rate is reported to be high [15]. The specificity of the classifiers used in this study was 97.8% for ResNet18, 98.1% for ResNet50, and 98.0% for ResNet101. A low false-positive rate and high NPV indicate its usefulness as a diagnostic aid that may reduce the burden on physicians. One limitation of this study is that the dataset we used consisted of mammographic images from Europeans, Americans, and other foreign nationals. Japanese individuals generally have more dense breasts and a smaller breast size than Europeans and Americans.