# Two-Stage Deep Learning Method for Breast Cancer Detection Using High-Resolution Mammogram Images
## 3 Methodology
### RoI Generation
The training process follows the same procedures as discussed before (i.e., breast region extraction, image enhancement, square patches generation, bounding box mapping, duplication removal). ## 4 Experimental Results and Discussion

Our experimental evaluation has three major objectives. The first objective is to evaluate the detection accuracy of the Faster R-CNN model in comparison to other state-of-the-art object detection models. Since the performance of tumor classification is heavily dependent on breast tumor detection, we need to assure that all tumors are present in at least one of the generated patches. To this extent, we also need to check if the entire breast region is clearly extracted from the original mammogram. The second objective is to show the overall performance improvement of the proposed model over conventional detection algorithms (e.g., original Faster R-CNN). This validates the importance of square patches generation (RoI) since we train the original Faster R-CNN model on the whole image. To this extent, we demonstrate comparisons between our method and the original Faster R-CNN model. The third objective is to transfer the model knowledge for the INbreast database and conduct comparison experiments with existing breast cancer diagnosis approaches. All experiments are conducted using 2\(\times\) NVIDIA Tesla V100 32 GB GPUs. Training in our machine took about 27 h. In addition, the inference time takes less than 4 s for each request (inputted mammogram), which is satisfactory because breast cancer diagnosis does not require real-time processing. Firstly, it is important to report that breast area extraction in the first stage extracted breast area with mean IoI of 0.91 and standard deviation of 0.014 for training samples. When cross-referenced against annotated tumor data, a zero bounding box was found outside the extracted breast region, which means that the first pre-processing step was successful in accurately extracting the breast region from the whole mammogram image. Next, square patches are generated using 1567 training data, and object detection models are trained on these patches (patches not including an annotation are excluded from training). Table 2 shows the detection accuracy of state-of-the-art object detection models. For our dataset, Faster R-CNN performed the best, followed by the SSD detection model. YOLO has better recall but lower mean IoU score compared to Mask R-CNN, which can be due to anchor limitations for each selected grid. In this study, computational complexity is not a main concern. In addition, since Faster R-CNN achieved the best results, we carry on following experiments using Faster R-CNN. Furthermore, because Mask R-CNN has a built-in masking (segmentation) module, it is interesting to examine its performance further. In the second part of the experiment, we compare the performance of detections using the BI-RADS lexicon. The object detection model is trained on 4124 patches generated from training data of 1567 mammography images. The remaining patches that do not contain any labeled masses/micros are discarded. In addition, for comparison, we trained the original Faster R-CNN model on 1567 whole mammogram images. To ensure fairness, we trained both models for 300 epochs on the same training conditions (e.g., the same GPU and machine). Figure 6 shows the results of our two-stage model and the original Faster R-CNN model on sample images. It is seen that our two-stage model can detect BI-RADS 2 far better than the 'one-stage' model because more information can be preserved by generating patches from extracted RoI. This validates that our method effectively solves the issue of the huge ratio difference of small lesions to the original input size by avoiding the proportion of breast lesions from being underrepresented. Moreover, our model eliminates false detections, which contributes to significant performance improvement, as seen in Figure 6a where original Faster R-CNN falsely detected BI-RADS 4.