# Region-of-Interest Optimization for Deep-Learning-Based Breast Cancer Detection in Mammograms
## 3 Experiment Results
### Classification  
The proposed ROI optimization and breast cancer classification method was evaluated on six distinct datasets: VinDr-Mammo, MiniDDSM, CMMD, CDD-CESM, BMCD, and RSNA. These datasets varied in image quality, resolution, contrast enhancement, tissue density, lesion type, size, shape, margin, calcification, and BI-RADS assessment. Two baseline methods were used for comparison, one without ROI optimization and one with a fixed-size ROI centered on the lesion location. Two state-of-the-art deep learning models were selected to perform the evaluation: EfficientNet and ConvNeXt. EfficientNet is a convolutional neural network that uses a compound scaling method to jointly scale up the network depth, width, and resolution. ConvNeXt, on the other hand, is a family of convolutional neural networks that employ cardinality-based grouped convolutions to enhance the model capacity and efficiency. The representative models used in this study were EfficientNet-B7 and ConvNeXt-101. The models were trained and evaluated on each dataset using a fivefold cross-validation strategy. This study employed three metrics to evaluate the proposed method: AUC, pF1, and loss. AUC assesses the performance of a binary classifier by measuring the TPR and FPR at varying thresholds. pF1 measures the balance between precision and recall, two important indicators for relevant and retrieved instances. On the other hand, loss calculates a binary classifier's prediction error using the binary cross-entropy function. A higher AUC and pF1 and a lower loss indicate better performance. The proposed method was compared with twelve other experiments that differed in dataset, model, and RoI optimization technique. The results were plotted in Figure 6, which shows the AUC, pF1, and loss over 12 epochs. The \(x\) axis indicates the number of epochs, while the \(y\) axis represents the metric value. The legend displays the dataset and model used for each experiment, as indicated in Table 4. Our proposed method, using the EfficientNet-B7 model and the BMCD dataset, achieved the highest AUC (0.98), pF1 (0.89), and lowest loss (0.0071), demonstrating its accuracy in breast cancer classification. Figure 5: Examples of data after applying ROI optimization method. Table 3 shows that the classification process worked well. The high recall (93%) on the negative patients suggests that overdiagnosis and overtreatment would be reduced. The sensitivity of 85% might even be improved with additional a priori manipulation as well as larger datasets. Table 4 shows the results. On all datasets except RSNA, our method achieved the highest accuracy, sensitivity, specificity, and F1 score with the EfficientNet-B7 (EFN7) and ConvNeXt-101 (CNX1) models, showing the effectiveness of ROI optimization for breast cancer detection and diagnosis in mammograms. Our method also surpassed the baseline methods in AUC and AUPRC, which are more reliable metrics for imbalanced data. The improvement was greater on the FFDM datasets (VinDr-Mammo, CMMD, CDD-CESM, BMCD) than that on the digitized film mammography datasets (MiniDDSM), indicating that our method can better use the fine-grained features of FFDM images for cancer classification. Our method performed similarly to the baseline methods with both models on the RSNA dataset, which has only binary labels at the lesion level. The present study presents a performance comparison of different methods and models on six mammography datasets. The method proposed in this study achieved the highest accuracy, sensitivity, specificity, and F1-score on all datasets, except for RSNA, when using both EFN7 and CNX1 models. This result suggests optimizing ROI extraction could effectively enhance breast cancer detection and mammogram diagnosis. Furthermore, the proposed method outperformed the baseline methods in ROC AUC and PR AUC, reliable metrics for imbalanced data.