## 1 Introduction  
Dermatological diseases are common in the medical field, and among them, skin pigmentary lesions are primarily caused by an increase or decrease in pigment, leading to changes in skin color. Melanoma is the most severe form of skin cancer within skin pigmentary lesions. In the early stages of the disease, melanoma is easily confused with other benign pigmentary skin lesions [1], and when the disease progresses to an advanced stage, treatment becomes significantly more difficult. Therefore, early detection of this disease is crucial. Due to factors such as hair, color, and blood vessel [2, 3] distribution on the skin surface, as well as the low contrast between diseased and healthy skin, even experienced clinicians often struggle to accurately identify the lesion areas, which can hinder the diagnosis of malignant melanoma [4]. In the medical field, medical image segmentation technology has evolved from manual segmentation to semi-automated and fully automated segmentation, with progressively improved segmentation results [5]. With the increasing size, complexity, and quantity of medical images, traditional machine learning methods and manual segmentation techniques are no longer sufficient to meet the demands of modern healthcare. The introduction of deep learning methods has, to some extent, addressed this challenge [6], significantly enhancing the efficiency and accuracy of image segmentation. However, the automatic segmentation of skin lesion images still faces several significant challenges, including hair occlusion [7], ambiguous lesion boundaries [8], low contrast between foreground and background [9], and high intra-class variability of lesion appearances [10]. These factors place substantial pressure on the discriminative and generalization capabilities of segmentation models. Therefore, the development of more robust segmentation algorithms is of critical importance for enhancing the accuracy and reliability of early melanoma detection [11]. With the rapid development of deep learning technology, image processing capabilities have significantly improved, and image segmentation has become increasingly widespread in the medical field. Computer-Aided Diagnosis (CAD) [12] has become an important tool in clinical diagnostics. Computer-aided diagnostic technology enables accurate segmentation of skin lesion areas, significantly improving the efficiency of clinical diagnosis. Traditional segmentation methods, including thresholding [13], region-based segmentation [14], edge-based segmentation [15], and support vector machine (SVM)-based techniques [16], are limited in their ability to extract high-level semantic features and often struggle to distinguish complex structures. To address common issues in skin lesion image segmentation, such as edge loss and insufficient segmentation accuracy, researchers have proposed various improvement algorithms. [PERSON] et al. [17] pioneered the Fully Convolutional Network (FCN), enabling pixel-level image segmentation in an end-to-end manner, thus laying the foundation for semantic segmentation. Building upon this, a significant breakthrough in medical image segmentation came with the U-Net architecture proposed by [PERSON] et al. [18], which employs a symmetric encoder-decoder structure and introduces skip connections to mitigate feature loss during up-sampling and down-sampling, effectively merging high-level and low-level semantic features, thereby promoting the widespread application of medical image segmentation. Subsequently, [PERSON] et al. [19] introduced the Attention-UNet by embedding an attention mechanism within U-Net, adaptively adjusting the encoder output to extract more effective features. [PERSON] et al.