## 6 Discussion  
We employed the two-views (CC and MLO) technique to construct and evaluate a proposed multi-label classification of breast cancer into eight labels corresponding to three groups, simultaneously, i.e., density (I, II, III, or IV), abnormality type/findings (mass or calcification), and severity level/pathology (benign or malignant). The CBIS-DDSM benchmark dataset was used to decide which technique is more suitable for this task; for example, it was used to evaluate the effect of different configurations, backbone models, fusion methods, and so on. The SOTA backbone model was used as the core component of the model to enable the automatic extraction of the features without human intervention.  
By relying on both views, the system can detect and identify the abnormalities that might not exist and are visible in a single view. Furthermore, certain irregularities might only be seen in one view and not the other; accordingly, using both views raises the chance of identifying the abnormalities and decreases the possibility of missing and neglecting any potential concerns and important information.  
We examined the SOTA CNN and transformer models, including ConvNeXt and the Swin transformer, and concluded that ConvNeXt was the best model for our proposed CAD system.  
After evaluating the performance of each model, we found that ConvNeXt was the most appropriate option for our specific tasks. ConvNeXt outperformed other architectures due to its use of depth-wise separable convolutions. These convolutions required fewer learnable parameters than traditional convolutional layers, making the network more effective and reducing overfitting. In addition, it can be adaptive to the data by dynamically building networks with a varying number of layers, depending on the complexity of the data. This makes the architecture more adaptable and flexible to several tasks and allows it to extract important features more efficiently.  
Moreover, ConvNeXt utilizes multi-scale processing. This allows for extracting the features from multiple levels of abstraction. This helps the model to extract and capture extra related information and enhance the performance of the introduced system.  
Three different fusion techniques were examined. The average-wise method was selected based on the results as it gave the best performance among the methods.  
As the proposed system classifies the input ROIs into three non-overlapped groups, integrating three branches into the end of the backbone model assists in enhancing the performance as it helps improve the feature representation and task-specific learning. Each branch of the multi-branch architecture focuses on learning features specific to a particular task, leading to more effective feature representation and task-specific learning. This helps the model capture more task-related features, yielding a better performance on that task, leading to improved performance.  
On the other hand, incorporating a channel attention mechanism for each task in the classification layer of a deep neural network has a slightly positive impact on the  
Figure 6: Visualization of the distribution of features (**a**), (**b**), and (**c**) indicate the distribution of the features among density, abnormality type (findings), and severity level (pathology), respectively.  
model's performance. Channel attention leads the model to focus on the significant relevant channels and suppress noninformative ones.  
Incorporating channel attention into ConvNeXt enables the selective enhancement of the most informative channels in the feature maps. It leads to a more effective representation of features and, eventually, results in enhanced model performance.