# Region-of-Interest Optimization for Deep-Learning-Based Breast Cancer Detection in Mammograms
## 3 Experiment Results  
### ROI Method with YOLOX Model  
The performance of different YOLOX models on two datasets, namely, new validation and remake validation, was compared in this study. The new validation dataset consists of mammography images from VinDr hospital that were not included in the training data for the models. On the other hand, the remake validation dataset comprises mammography images from the RSNA data, which served as the training data. Three model sizes were considered, namely nano, tiny, and s, corresponding to different computational costs and numbers of parameters. Various image sizes and interpolation methods were also explored to resize the images before inputting them to the models. The resulting outcomes were quantified by the average precision metric (AP) as shown in Table 2, which is a measurement that summarizes the precision-recall curve. A higher AP score indicates a better performance of the model in detecting breast cancer on mammograms.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Size** & **Image Size** & **Interpolation** & **AP New Validation (\%)** & **AP Remake Validation (\%)** \\ \hline Nano 1 & 416 & LINEAR & 96.26 & 94.21 \\ Nano 2 & 416 & AREA & 94.09 & 91.60 \\ Nano 3 & 640 & LINEAR & 95.85 & 88.40 \\ Nano 4 & 768 & LINEAR & 96.22 & 82.09 \\ Nano 5 & 1024  
The nanodata with an image size of 416 and linear interpolation demonstrated superior performance on both validation datasets, with AP scores of 96.26% and 94.21%. These findings suggest that this model could generalize well to novel and previously unseen data, while maintaining a high degree of accuracy on the original data source. Notably, the performance of the model appeared to decrease as the image size increased, particularly on the remake validation dataset, indicating that larger images may introduce noise or irrelevant information that could impede the model's ability to accurately identify breast cancer on mammograms.  
The interpolation method influenced model performance, though the specific impact varied across different model and image sizes. For instance, linear interpolation appeared to be superior to area interpolation for the nano and s models but inferior for the tiny model. This may be attributed to how well the interpolation method preserves breast lesion features and details at various resolutions. Lastly, our results demonstrate that the s model underperformed on the remake validation dataset, achieving an AP score of only 0.86, regardless of image size or interpolation method. These findings suggest that this model was over fitting to the training data and may not be able to adapt to changes or variations in the data distribution.  
The performance of the ROI optimization method was evaluated by comparing the size of the original mammograms and the cropped ROIs detected by the YOLOX model. Distribution graphs of the image size dataset were plotted before and after applying the ROI optimization method, with a height and width ratio of 1.018, as depicted in Figure 4. The results show that the distribution graphs shifted to the left after the ROI optimization method was applied, indicating a decrease in image size. The mean image size of data decreased by 76.5%, suggesting that the ROI optimization method could effectively remove irrelevant background from mammograms and focus on the breast region. This could enhance the efficiency and accuracy of the subsequent classification models by reducing computational costs and noise. Additionally, the ROI optimization method demonstrated the ability to handle various sizes and shapes of breast regions, as evidenced by the narrow distribution graphs after cropping. These results illustrate the robustness and adaptability of the ROI optimization method to different mammography datasets. Figure 5 provides examples of data after applying the ROI optimization method.  
Figure 4: Distribution graphs of the image size dataset before and after applying our ROI optimization method.