# Two-Stage Deep Learning Method for Breast Cancer Detection Using High-Resolution Mammogram Images
## 1 Introduction
Although this is the main motivation behind breast cancer diagnosis, such _binary_ classification-based models cannot directly assist radiologists in breast cancer screening and reporting. In the majority of real-world scenarios and applications, the Breast Imaging Reporting and Database System (BI-RADS) [20; 21; 22] score, which is a standard scoring system, is used by radiologists to report mammogram results. BI-RADS scores are classified into incomplete assessment (category 0) and complete assessment (categories 1, 2, 3, 4, 5, 6). The description of each category is presented in Table 1. The classification of the corresponding BI-RADS score recommends specific clinical screening routines which are important for appropriate patient treatment and follow-up screening. Moreover, BI-RADS scores provide a wide range of suspicion of malignancy and are considered more descriptive than classifying tumors into just two classes: benign and malignant. Therefore, it is important to build CAD systems according to the BI-RADS lexicon. Unfortunately, despite the presence of BI-RADS scores in the above-mentioned DDSM and INbreast databases, prior studies [10; 15; 17; 18; 19] classify the detected tumors into binary classes (e.g., BI-RADS 1, 2 as benign and BI-RADS 4, 5, 6 as malignant). In this study, we aim to propose a model according to the BI-RADS lexicon and improve classification specificity and sensitivity by thoroughly examining distinct intra-class features and attributes (e.g., BI-RADS 4c vs. 5, BI-RADS 4a vs. 4b), which is not the case for binary classification. Finally, the lack of large open datasets of modern high-resolution digital mammograms containing labeled BI-RADS scores, annotated RoIs, bounding-box coordinates, and masks creates challenges in developing unified and robust CAD systems. To overcome this issue, prior studies [10; 17; 18] used pre-trained models and transferred the network _knowledge_ to diagnose breast cancer in smaller datasets. For instance, [PERSON] et al. [17] trained the original model on DDSM database of digitized film mammograms and, using _transfer learning_, fine-tuned the network parameters on INbreast [14] training data to build a classifier for INbreast data. In addition, as previously discussed, [PERSON] et al. [10] also carried out similar experiments. In this study, we introduce our newly collected high-resolution mammograms dataset with accurately labeled BI-RADS scores. In this paper, we propose a two-stage deep learning method for accurate breast cancer detection and classification. Stage 1 extracts 'potential' RoI patches from the original input data without the need for human labor, whereas stage 2 detects breast masses, improves bounding box regions, and classifies them into the BI-RADS categories. Numerous data pre-processing steps are introduced and a specifically-designed CNN-based tumor classification model is proposed to further improve the classification accuracy obtained from the detection model. The key contributions are as follows:  
1. We collected a new dataset of high-resolution mammography images and annotated them with BI-RADS lexicon, which provides a more reliable and a wider range of suspicion of malignancy;
2. We propose a two-stage deep learning approach to significantly increase detection accuracy. To this extent, we introduce square patch generation, bounding-box mapping, and duplication removal algorithms to automatically extract RoIs from the input data, and map back the patch detections into the original image without human supervision and labor;
3. We design a tumor classification model to improve the classification accuracy of breast tumors obtained by the object detection model. The output of the classification model is combined with the output scores of the object detection model's classifier to get the final output.