## 1 Introduction  
Cancer is one of the leading causes of death worldwide. Breast cancer is the most common cancer in women and the leading cause of cancer-related death among women [1]. Early diagnosis is very important because breast cancer has a high chance of cure if it is diagnosed early and before it has metastasized [2]. A variety of imaging modalities are used to diagnose breast cancer, including mammography, magnetic resonance imaging (MRI), ultrasonography, histopathology, computed tomography (CT), positron emission tomography (PET), and thermography [3]. Mammography is one of the modalities used for breast cancer imaging and is routinely used. Mammography has been reported to detect approximately 80-90% of asymptomatic breast cancer cases [4], significantly reducing patient mortality [5]. When radiologists read screening mammograms, they look primarily for soft tissue findings and calcification clusters. Soft tissue findings can vary from masses (having various shapes and edges, such as smooth, spiny, indistinct, or irregular), asymmetry (dense tissue in one breast with no counterpart in the opposite breast), and disorganized architecture (abnormal composition of fibroglandular tissue) [6]. Calcification is caused by the deposition of calcium oxalate and calcium phosphate in the breast tissue and appears as bright white spots on mammographic images [7]. Calcifications may indicate breast cancer or precancerous changes in breast tissue, and the morphology and distribution of calcifications are important biomarkers of malignancy [6]. Benign calcifications are generally large, coarse, round, and smooth at the edges, whereas malignant calcifications are generally small and often referred to as microcalcifications [8]. Clustered microcalcifications are found in 30-50% of the cancer cases diagnosed by mammography [4]. However, they are difficult to detect and may be overlooked in breast cancer screening due to their size (approximately 0.1-1 mm) and dense surrounding mammary tissue [4; 9]. Typical benign calcifications include cutaneous calcifications, vascular calcifications, coarse or popcorn-shaped calcifications, thick rod-shaped calcifications, round calcifications, oval or rim-shaped calcifications, milk of calcium calcifications, and suture calcifications. Calcifications of moderate concern include amorphous or indistinct, coarse, and heterogeneous calcifications. Calcifications of high malignant potential include those that are grouped or clustered, fine, linear, branched, pleomorphic, or numerous, and they are amenable to biopsy [8]. The distribution of the calcification indicates how the calcification is distributed throughout the breast, including diffuse/scattered, regional, clustered, linear, and segmental. In general, diffuse/scattered and regional calcifications are benign, while clustered calcifications suggest intermediate malignant potential, and linear and segmental calcifications are more likely to be malignant. Therefore, clustered, linear, and zonal distributions of calcifications are important [8; 10]. Thus, the morphology and distribution of calcifications are useful in differentiating benign from malignant disease [10], and it is very important for radiologists to detect and evaluate calcifications [8]. In a study on mammography imaging, researchers determined that Rh filtration and a W anode significantly improved image quality and dose efficiency compared to a Mo anode and Mo filtration [11]. In this way, various studies are being conducted in mammography, from imaging to diagnosis. In recent years, deep learning techniques have been used in a variety of fields [12; 13; 14; 15]. There have been various studies using deep learning techniques for breast cancer, especially in the field of mammography [16].