## 1 Introduction
However, the window sizes of the aforementioned Transformer-based networks can vary significantly between low-resolution pre-training and high-resolution fine-tuning. Currently, the most common approach is to perform bicubic interpolation on the position offset maps [12; 15]. This simple fix often leads to suboptimal results. To address this issue, we adopt the Swin Transformer V2 for feature extraction and introduce the log-spaced continuous position bias method. It applies a small meta-network to logarithmically spaced coordinate inputs to generate bias values for arbitrary coordinate ranges. This method effectively transfers models pre-trained under low-resolution images and windows to corresponding models with higher resolutions. To maintain a strong grasp of the underlying details, the Swin Transformer V2 is combined with ResNet50, and their multi-level features are input into the fusion module. The fusion module first refines the multi-level feature maps, enhances their robustness and resolution, and then applies self-attention and bilinear Hadamard products to adaptively select fusion information. The fused feature maps are then combined, and segmentation images are generated using gated skip connections [16]. This approach enables more accurate and robust segmentation results for polyps and skin. Moreover, the shape, location, and volume of skin melanoma vary among patients. The boundaries between lesion areas and surrounding normal tissues are often ambiguous, and significant variations in image color and contrast may arise due to disparities in imaging equipment and settings employed by different healthcare institutions [17]. A new method, designed by [PERSON] [18], is used to preprocess these images by skipping blank areas. It first identifies blank areas in the image through thresholding or other image processing techniques and marks these areas as regions that do not participate in training and segmentation. During training and inference, the model only processes non-blank areas. However, this method does not have a direct impact on dealing with blurred boundaries. In [19; 20; 21; 22], the random displacement field method was adopted to achieve the smooth elastic deformation of medical images, increasing the diversity of image content. The application of the random displacement field method may increase image variation and texture to a certain extent, making some features of blurred boundaries more prominent and helping the model better adapt to situations with blurred boundaries. However, it should be noted that blurred boundaries are usually caused by the quality of the image itself or limitations in the imaging process, and the random displacement field method cannot be a reliable solution. In response to the above issues, we propose a new preprocessing module that combines fuzzy logic. Fuzzy logic is a logical reasoning method for dealing with uncertainty and fuzziness and can be applied to image processing and segmentation tasks to solve the fuzzy boundary problem. Fuzzy logic classifies pixels into multiple fuzzy sets based on their grayscale values or other features and uses fuzzy rules to infer the degree of uncertainty of the boundary. In this way, fuzzy boundaries can be represented in a continuous and fuzzy manner, rather than just a binary boundary. In this paper, by using histogram equalization, wavelet transform, and the uncertainty of membership functions in fuzzy logic are utilized to address the medical image boundary fuzziness and imaging problems, it is applied to the input layer of the network to pre-label all pixels in the image, thereby improving the segmentation performance of the model on fuzzy boundaries. Our main contributions are summarized as follows:  
1. In order to effectively deal with fuzzy boundaries, we propose a fuzzy block preprocessing method.