## 2 Related Work  
Mammography is one of the diagnostic imaging modalities for breast cancer. It is an effective screening tool for breast cancer diagnosis and is the only method proven to reduce breast cancer mortality [4]. According to a recent report [5], it is one of the most effective ways to detect breast cancer in its early stages. Mammography detects approximately 80% to 90% of breast cancer cases in asymptomatic women and has been reported to reduce breast cancer mortality by 38-48% among those screened [6; 7]. This also has been reported [8; 9; 10] in recent years to reduce mortality by 30% or mortality by 20%. Mammography is mainly used for qualitative diagnosis and to observe masses, focal asymmetric shadows, calcifications, and structural abnormalities. Calcification is a common finding in mammography. It is caused by the deposition of calcium oxalate and calcium phosphate in the breast tissue and appears as bright spots on mammography [11; 12]. The distribution of calcification is useful for differentiating benign from malignant disease. Diffuse or scattered calcifications are generally benign, while zonal or linear calcifications are suspected to be malignant [13]. Because clustered microcalcifications are found in 30-50% of cases of cancer diagnosed by mammography, the detection, evaluation, and classification of calcifications are important [6; 7]. However, microcalcifications can be difficult to accurately detect and diagnose due to their size, shape, and heterogeneity in the surrounding tissue [6].  
Computer-aided diagnosis (CAD) systems have been used in mammography to assist in the reading of mammograms and were developed to detect abnormal breast tissue and reduce the number of false-negative results for breast cancer detected by radiologists [14]. The sensitivity and specificity of CAD for all breast lesions have been reported to be 54% and 16%, respectively. CAD can easily misclassify parenchyma, connective tissue, and blood vessels as breast lesions, vascular calcifications, and axillae as microcalcifications. Because of its low specificity and high false-positive rate, CAD should not be used alone in mammography for breast examinations [15].  
Deep learning technologies, specifically convolutional neural networks (CNNs), have recently been used in a variety of fields, including medical imaging [16; 17]. Deep learning techniques have a wide range of applications, including classification [18; 19], object detection [20; 21], semantic segmentation [21; 22; 23], and regression [24; 25; 26]. Deep learning techniques have also been applied to breast diagnostics. For MRI, studies have used U-Net for breast mass segmentation [27] and ResNet18 pretrained on ImageNet for classification of benign and malignant breast tumors [28]. These studies are limited due to the small number of available breast MRI data and often rely on transfer learning. Similarly, deep learning methods for breast ultrasound also typically use transfer learning and pretraining with ImageNet due to the small number of available breast ultrasound image training sets [18]. Deep learning models in mammography have been used not only for the detection of potential malignancies but also for tasks like risk stratification, lesion classification, and prognostic evaluation based on mammographic patterns [29; 30]. Researchers have also explored the potential of employing transfer learning to overcome challenges associated with the limited availability of supervised image training mammography data [31].