# An Improved Multi-Scale Feature Fusion for Skin Lesion Segmentation
## 3 Proposed Methods
### Overall Structure of the Network  
In this paper, we introduce a new image segmentation model named REDAUNet, which is based on the UNet3+ model. The network model combines the cross-residual module, the efficient channel attention (ECA) mechanism, and the DenseASPP module. Figure 2 shows the overall structure of the network model, which can effectively extract feature information and accurately segment images.  
In the figure, the size of the feature map is shown in the left corner of each block, while at the top is the number of channels. The images undergo five downsampling operations to gradually reduce their size and increase the receptive field of the convolutional kernel, thus obtaining more global information. Additionally, the ECA mechanism is adopted to make the model focus more on lesion areas and effectively extract features related to the disease. During the upsampling process, multi-level feature fusion is performed, and the channel limit in the output is 160 to reduce computation. Furthermore, we modified the bottleneck layer between the upsampling and downsampling layers and integrated a newly designed DenseASPP module. The module uses four atrous convolutional layers with different dilation rates to acquire lesion features of different sizes. The detailed segmentation pipeline of the proposed segmentation model includes the following steps:  
* Step 1: Input image preprocessing. Preprocess the original image, including resizing, normalization, denoising, etc., to ensure that the input data meet the requirements of the model;
* Step 2: Encoder. The preprocessed image enters the encoder, where a series of convolutional layers and pooling operations are applied to gradually reduce the resolution of the image and extract multi-level feature representations. These feature representations have different semantic information and receptive field sizes, which are used to capture context information at different scales in the image;
* Step 3: Attention mechanism. After each layer of the encoder, an attention mechanism is introduced to add attention weights to the feature maps of each encoder. This enhances the model's focus on key regions. The attention mechanism can dynamically adjust the attention weights based on the relationship between the current pixel and its surrounding pixels, enabling the model to more accurately locate and segment the target region;
* Step 4: DenseASPP. The DenseASPP module is placed between the encoder and decoder. It effectively captures semantic information at different levels and scales in the image by introducing multiple scales of receptive fields and atrous convolutions with different sampling rates. In the decoding stage, it enables more accurate pixel-level predictions. It improves the receptive field of the segmentation model, thereby better understanding the contextual information and semantic content of the image;
* Step 5: Decoder. The feature maps from the encoder are passed to the decoder through upsampling and skip connections. The decoder gradually restores the resolution of the image and fuses it with the corresponding level of encoder features. By using deconvolution and upsampling operations in the decoder, the detailed information of the image is restored, and more accurate segmentation results are predicted by utilizing the semantic features extracted by the encoder through skip connections;  
Figure 2: The architecture of the proposed network.  
* Step 6: Segmentation probability output. The segmentation prediction generated by the decoder is usually a segmentation mask of the same size as the input image. Each pixel in the image is assigned a probability value indicating its probability of belonging to the target region;
* Step 7: Segmentation image output. Set a probability threshold for the image and set pixels with values greater than the threshold to 255 and pixels with values less than the threshold to 0. Finally, output the processed segmentation result as a segmentation mask.