# Two-Stage Deep Learning Method for Breast Cancer Detection Using High-Resolution Mammogram Images
## 1 Introduction  
Accurate and early detection of breast cancer is substantial to fight against the second highest cause of mortality in women. According to the World Health Organization (WHO), new cases of breast cancer were diagnosed in over 2 million women globally in 2020 and are predicted to increase by 70% in the next 20 years [1]. Early detection of breast cancer potentially doubles the survival rate. Breast cancer diagnosis includes correctly identifying each stage and classifying detected breast tumors and abnormalities into proper categories. In order to assist radiologists and oncologists in diagnosing breast cancer in a fast and reliable manner, many computer-aided diagnosis (CAD) systems have been developed over the last two decades [2]. Unfortunately, earlier CAD systems did not produce significant improvements in day-to-day breast cancer diagnosis in clinical use [3, 4]. After the 'boom' of deep learning (DL), DL-based CAD systems and other computer vision and object recognition methods brought success to many areas of medicine, from day-to-day healthcare practices to comprehensive medical applications [5, 6, 7, 8]. Currently, there are many DL-based CAD systems that can be used to assist radiologists in breast cancer screening, monitoring, and diagnosis. Recent studies [9, 10] suggest that DL-based object detection and segmentation models, in particular, can potentially perform as well as radiologists in standalone mode by producing accurate and reliable results. However, before directly applying such models to diagnose breast cancer, especially when using high-resolution digital mammography images, certain challenges and issues need to be addressed carefully. One of the main challenges in accurately detecting breast masses (mass and tumor are used interchangeably) from high-resolution mammography images is that the ratio of somebreast masses to the overall image size is too small. For instance, some clinically significant abnormal growth or even potentially cancerous tumors may lie within 100 \(\times\) 100-pixel regions [10], whereas the size of digital mammography images is usually 5900 \(\times\) 4700 pixels. Furthermore, some breast masses can still be greater in size but have very low contrast which can be overlooked when examining the full-sized high-resolution mammograms. Some prior work [11; 12; 13] addressed this issue by using manually annotated lesions to narrow down the potential region of interest (RoI) so that deep learning models can focus only on that RoI. However, manually annotating RoI is laborious and ultimately restricts to having fully automated real-world applications due to the absence of such annotated RoI for in-the-wild data. [PERSON] et al. [10] trained a model on a Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) database that contains annotated RoI and transferred the model RoI extraction layers for the INbreast dataset [14]. However, this approach still requires manually annotated RoIs for initial network training. Other studies [5; 6; 15] attempted to train deep convolutional neural networks (CNN) using the whole mammogram images. Unfortunately, due to the input size restrictions and pooling layers (i.e., downsampling), even state-of-the-art object detection models miss some of the significant lesions or micro-calcifications. [PERSON] et al. [15] used a Single-shot detector (SSD) [16] object detection model for the whole mammogram to obtain RoIs for further tumor shape classification. However, the accuracy of the classification is based on the premise that SSD is able to detect all tumors, which in general, is not true. Considering these issues, appropriate automatic RoI extraction methods should be exploited. Another common issue in breast cancer diagnosis is that most of the existing DL-based models classify detected tumors/abnormalities into benign and malignant categories [10; 15; 17; 18; 19].