# Region-of-Interest Optimization for Deep-Learning-Based Breast Cancer Detection in Mammograms
## 2 Materials and Methods
### Models  
The proposed breast cancer detection method on mammograms utilizes two deep-learning models: EfficientNet and ConvNeXt. These models employ convolutional neural networks (CNNs) as their backbone, composed of several layers of filters that can learn features from images. Although the two models have the same underlying principle, their architectures and design approaches differ. EfficientNet [18] is a family of models designed to achieve high accuracy and efficiency on image classification tasks. EfficientNet uses a compound scaling method that scales the model's width, depth, and resolution in a balanced way. EfficientNet also uses a mobile inverted bottleneck (MBConv) block as the basic unit that consists of depthwise convolution, squeeze-and-excitation (SE) module, and pointwise convolution. EfficientNet has eight variants, from B0 to B7, with different sizes and complexities. We used EfficientNet-B0 as our base model, which has 5.3 million parameters and 0.39 billion FLOPs. ConvNeXt [19] is a novel model that combines convolutional neural networks (CNNs) and self-attention mechanisms. ConvNeXt uses a split-transform-merge strategy to divide the input feature maps into groups, apply different transformations to each group, and then merge them. ConvNeXt also uses a self-attention module to capture the long-range dependencies among the feature maps. ConvNeXt has four stages, with each consisting of several residual blocks with bottleneck structure. We used ConvNeXt-50 as our base model, with 25 million parameters and 4.3 billion FLOPs. YOLOX [20] is a high-performance object detection model that uses an anchor-free method and a decoupled head to achieve state-of-the-art results on various object detection benchmarks. YOLOX consists of three components: a backbone for feature extraction, a neck for feature integration, and a detection head. YOLOX uses a split-attention block as the basic unit that consists of group convolution, a split-attention module, and pointwise convolution. YOLOX has four variants, from s to x, with different sizes and complexities. We used YOLOX-s as our base model, which has 9 million parameters and 26.8 billion FLOPs. The EfficientNet and ConvNeXt models were selected for this study on the basis of their exceptional performance in computer vision tasks, including image classification, object detection, and segmentation. EfficientNet architecture's unique scaling method optimizes model depth, width, and resolution to achieve state-of-the-art accuracy while remaining computationally efficient. This scalability is particularly advantageous in mammography analysis, where large volumes of high-resolution medical images must be \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Data** & **Source** & **Country** & \begin{tabular}{c} **Number of** \\ **Exams** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Images** \\ \end{tabular} & \
\begin{tabular}{c} **Number of** \\ **Benign Cases** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **Malignant** \\ **Cases** \\ \end{tabular} \\ \hline VinDr-Mammo & VinDr Hospital & Vietnam & 5000 & 20,000 & 3500 & 1500 \\ MiniDDSM & DDSM & USA & 2506 & 10,024 & 1506 & 1000 \\ CMMD & Various hospitals & China & 9000 & 36,000 & 6000 & 3000 \\ CDD-CESM & Various hospitals & Spain & 1000 & 2000 & 500 & 500 \\ BMCD & Various hospitals & Turkey & 1500 & 3000 & 750 & 750 \\ RSNA & Various institutions & Multiple countries & 2000 & 8000 & - & - \\ Total & - & - & 21,006 & 79,024 & 12,256 (61.4\%) & 6750 (33.9\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary statistics of the combined datasets. Figure 1: Examples of mammography images from different sources and modalities with a benign or malignant label. processed. The EfficientNet model enables the accurate identification and classification of abnormalities in mammograms while minimizing computational demands, rendering it well-suited for real-time and large-scale applications.