## 2 Methodology
### Suggested U-Net Model  
U-Net is a convolutional neural network (CNN) architecture used for image segmentation. It was developed by [PERSON] et al. in 2015 for biomedical image segmentation. The architecture is called U-Net due to its U-shape structure. The network has an encoder-decoder structure with skip connections between corresponding layers in the encoder and decoder. The skip connections concatenate feature maps from the encoder to the decoder, which helps preserve spatial information and improve segmentation accuracy [29; 30]. One of the main advantages of U-Net is its ability to work with a small amount of training data. It achieves this through data augmentation techniques such as flipping, rotating, and scaling of images. The network is also very effective in handling complex and irregular shapes in image segmentation, which makes it ideal for biomedical image segmentation. Additionally, U-Net is computationally efficient and can be trained on a single GPU, making it accessible to researchers and practitioners with limited resources [31; 32]. Another advantage of U-Net is its ability to handle multi-class segmentation. The network can segment an image into multiple classes, making it useful for a variety of applications such as identifying different types of cells in a medical image or identifying different types of objects in a satellite image. The skip connections in U-Net make it easier to train the network to distinguish between different classes, improving segmentation accuracy [33; 34]. However, one disadvantage of U-Net is its tendency to produce over-segmentation in certain cases. Over-segmentation occurs when the network generates too many small segments instead of a few larger ones, which can reduce the accuracy of the segmentation. Another disadvantage of U-Net is its sensitivity to the quality of the training data. If the training data are noisy or of low quality, they can negatively impact the segmentation accuracy [35; 36]. Our study introduces a modified version of the U-Net model specifically designed for breast tumor segmentation (See Figure 2). This model consists of three down-sampling and three up-sampling layers, which enable the network to effectively capture image features at multiple scales. To further enhance feature extraction, we also incorporated three Cascade  
Figure 1: Demonstration of spatial attention mechanism block used in our study. Feature Extraction (CFE) blocks into the down-sampling path. The detail of the CFE block is demonstrated in Figure 3.  
Each cascade feature extraction block consists of four feature extraction paths with varying receptive fields. The first two paths employ a combination of scale and shift (Scale/Shift) and negation layers, which are commonly used in image processing tasks to improve feature extraction. The output from each path is then concatenated before being passed to the next stage of the cascade feature extraction block. In deep learning models, a shift (scale) layer is a type of layer that performs a linear transformation of the input data, scaling the input by learned weights and biases. This scaling operation helps to normalize the input data, improving the stability and efficiency of the model during training [37; 38]. Figure 3: Details of the CFE block used in this study. Figure 2: The proposed U-Net architecture and attention mechanism. The shift layer is commonly used in convolutional neural networks (CNNs) for image processing tasks, where it is often paired with other layers such as convolutional layers and activation functions. The primary advantage of using a shift layer is that it helps to normalize the input data to a similar scale, which can improve the convergence of the model during training. This is because the scale of the input data can greatly affect the gradients during backpropagation, making it difficult for the model to converge to a stable solution [38; 39].