## 2 Methodology
### Suggested U-Net Model
Another advantage of using a shift layer is that it can help to reduce overfitting, which occurs when the model becomes too complex and begins to memorize the training data instead of generalizing to new data. By normalizing the input data, the shift layer can prevent the model from learning spurious patterns that may be present in the training data but do not generalize to new data [37; 39]. A negation layer is a type of layer that performs a pointwise operation on the input data by negating it, i.e., multiplying the input by \(-1\). Negation layers are commonly used in conjunction with other layers such as convolutional layers and activation functions [40; 41]. The primary advantage of using a negation layer is that it can help to introduce more diversity and complexity into the model's learned representations. By negating the input data, the model can learn to recognize features and patterns that are not present in the original input, but are still relevant to the task at hand. This can be particularly useful in scenarios where the input data are highly structured or predictable, as this allows the model to explore a wider range of possible solutions [40; 41; 42]. Another advantage of using a negation layer is that it can help to reduce overfitting. By introducing more diversity into the model's learned representations, the negation layer can prevent the model from overfitting to the training data and improve its ability to generalize to new data [40; 41; 42]. To improve the accuracy of the segmentation results, we included an attention mechanism layer that highlights the most important features in the feature maps obtained from the cascade feature extraction blocks. This is achieved by using the attention modules to selectively weight the feature maps based on their relevance to the segmentation task. The proposed modified U-Net model has shown promising results in breast tumor segmentation, outperforming traditional U-Net models in terms of accuracy and robustness. The incorporation of cascade feature extraction blocks and attention mechanisms has allowed the network to better capture the subtle features of breast tumors, resulting in more accurate segmentation results. Combining an attention mechanism technique, cascade feature extraction, and a U-Net model for breast tumor segmentation can bring several benefits to the segmentation task:  
1. Improved accuracy: The use of an attention mechanism can help to highlight the most important features in the feature maps of the U-Net model, allowing the model to focus on the relevant areas and improve its accuracy. Additionally, the cascade feature extraction block can help to extract more meaningful features from the input data, further improving the accuracy of the segmentation;
2. Reduced false positives and false negatives: By using the cascade feature extraction block and attention mechanism, the model can better distinguish between healthy tissue and tumor tissue, reducing the likelihood of false positives and false negatives in the segmentation results;
3. Robustness to noise and variability: The use of a U-Net model provides a robust framework for breast tumor segmentation, as it is able to handle noisy and variable input data. By combining this with the attention mechanism and cascade feature extraction, the model can better adapt to different types of input data and improve its segmentation accuracy;
4. Faster convergence and training: The attention mechanism and cascade feature extraction block can help to reduce the number of training iterations required for the model to converge, which can save time and computational resources during the training process;Generalizability: The use of a U-Net model, combined with the attention mechanism and cascade feature extraction, can provide a generalizable framework for breast tumor segmentation that can be applied to different types of data and imaging modalities. This can be particularly useful for clinical applications, where different types of imaging data may be available for different patients.