# Two-Stage Deep Learning Method for Breast Cancer Detection Using High-Resolution Mammogram Images
## 3 Methodology
### RoI Generation
Figure 7 shows the performance difference between our proposed two-stage method and the original Faster R-CNN model for each BI-RADS category. The difference is especially significant for BI-RADS 2 due to its very small size in comparison to the overall input image. Our method detects 439 more BI-RADS 2 and 3 classes compared to Faster R-CNN. In addition, it is clearly seen across all classes that our method outperforms the original Faster R-CNN model in terms of accuracy. For instance, our method correctly classified 23% of BI-RADS 4 classes (4a, 4b, and 4c). Lastly, the precision of BI-RADS 5 and 4c improved by 7%, which is substantial considering their importance (e.g., BI-RADS 5

Figure 6: Performance difference between one stage (on the left) and two-stage (on the right) Faster R-CNN detection models: (**a**) inference results on sample data #1; (**b**) inference results on sample data #2.

yields high suspicion of malignancy with a 95% chance of breast cancer). Overall, the mean average precision (mAP) improved from 0.85 to 0.92 with IoU of 0.45 (i.e., 0.92 [EMAIL_ADDRESS]). From the results, it is seen that our two-stage method that generates smaller patches and then uses them to obtain combined final detections proves to be efficient. Table 3 shows the confusion matrix for our model, where classification for each BI-RADS categories can be seen clearly. BI-RADS 2 is most likely to be misclassified as background and sometimes as BI-RADS 3. Furthermore, BI-RADS 5 achieved the best performance, which is desirable considering its importance. Moreover, by employing a supportive classification model, the accuracy for intra-classes improved by another 0.02 mAP. Table 4 shows the performance of the Faster R-CNN classifier, CNN-based classification model, and their combined results. Overall (combining two models), our method achieved 0.94 [EMAIL_ADDRESS]. Similarly, experiments with Mask R-CNN showed overall performance improvement from 0.83 (original Mask R-CNN model) to 0.92 mAP (our two-stage model with Mask \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **mAP** \\ \hline Faster R-CNN & 0.92 \\ Classification model & 0.93 \\ Combined results & 0.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mean average precision of classification models. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Predicted Results**} \\ \hline  & \multicolumn{2}{c}{**Background**} & **2** & **3** & **4a** & **4b** & **4c** & **5** \\ \hline \multirow{6}{*}{True Data} & Background & 0.90 & 0.05 & 0.02 & 0.02 & 0 & 0.01 & 0 \\  & 2 & 0.14 & 0.78 & 0.08 & 0 & 0 & 0 & 0 \\  & 3 & 0.07 & 0.05 & 0.88 & 0 & 0 & 0 & 0 \\ \cline{1-1}  & 4a & 0.01 & 0 & 0 & 0.96 & 0.02 & 0.01 & 0 \\ \cline{1-1}  & 4b & 0 & 0 & 0 & 0.02 & 0.96 & 0.01 & 0.01 \\ \cline{1-1}  & 4c & 0 & 0 & 0 & 0.01 & 0.03 & 0.96 & 0 \\ \cline{1-1}  & 5 & 0.01 & 0 & 0 & 0 & 0 & 0.01 & 0.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix for BI-RADS scores. The values are normalized so each row sums to one. Figure 7: Precision–recall curve for each BI-RADS class: (**a**) the original Faster R-CNN; (**b**) our model. R-CNN). However, Faster R-CNN outperforms Mask R-CNN, and the utilization of Mask R-CNN in real-world CAD systems can be useful for medical reporting because the model can generate masks along with detection bounding box coordinates. These experiment results prove that our patch generation method in the first stage improves the accuracy of detection and classification, especially for the detection of very small breast abnormalities (Figure 7). In the last part of the experiment, we perform transfer learning. We use our pre-trained model (trained on our private dataset) to learn features of INbreast database samples via fine-tuning. The proposed model achieved an AUC (i.e., area under the curve) score of 0.97. Table 5 shows comparison results with existing methods on the INbreast database.