# Region-of-Interest Optimization for Deep-Learning-Based Breast Cancer Detection in Mammograms
## 3 Experiment Results
### Detecting the Breast Cancer Area  
In this study, a novel method for detecting breast cancer in mammograms is presented, which leverages region of interest optimization and deep learning with gradient-weighted class activation mapping to generate bounding boxes. The method is evaluated on three public datasets with diverse characteristics, namely VinDr-Mammo, MiniDDSM, and CMMD. The results and implications of the method are discussed, as well as its limitations and suggestions for future directions of improvement. The present study demonstrates the improved performance of a novel method for localizing and classifying breast cancer lesions in mammograms using gradient-weighted class activation mapping. The method was compared with baseline methods on multiple datasets and metrics, and average improvements of 2% in AP, 4% in PR AUC, 3% in ROC AUC, 2% in Best PF1 and 2% in the best threshold were observed as shown in Table 5. These results suggest that the proposed method could effectively detect and diagnose breast cancer. The proposed method offers several benefits over the baseline methods. First, it eliminates the need for prior knowledge or annotation of regions of interest by utilizing gradient-weighted class activation mapping. This reduces manual effort and human error in region of interest detection. Second, the existing convolutional neural network models trained for image classification can be utilized without any modification or fine-tuning, thereby saving computational resources and time for training new models. Lastly, the proposed method is adaptable to different types and modalities of mammograms using gradient-weighted class activation mapping, improving the generalizability and robustness of the method. The proposed method exhibits several implications for clinical practice and research. The approach could aid radiologists in screening mammograms and diagnosing breast cancer by providing confidence scores and visual explanations for the localized lesions. Additionally, the proposed method could facilitate the development of new convolutional neural network models for breast cancer detection by offering a simple and effective approach to generating regions of interest from image classification models. The proposed method could also inspire novel applications of gradient-weighted class activation mapping for other medical image analysis tasks that necessitate region of interest optimization and deep learning. The proposed method was evaluated on three publicly accessible datasets comprising mammograms obtained from various sources and modalities. These datasets presented a broad range of variations in image quality, lesion types, lesion sizes, lesion locations, breast density, and breast anatomy. Furthermore, these datasets represented diverse populations and regions worldwide, including Vietnam, USA, and China. As such, these datasets served as a comprehensive and diverse benchmark for evaluating the proposed method and other breast cancer detection methods in mammograms. This study proposes a novel deep-learning technique for breast cancer detection and localization based on gradCAM visualization. Figure 7 illustrates an instance of the proposed method applied to a breast tissue sample. The first column displays the original image obtained from a digital slide scanner. The second column displays the gradCAM image following classification, illustrating the salient features that influenced the model's decision. The third column displays the predicted tumor area mask obtained by applying a