# An Improved Multi-Scale Feature Fusion for Skin Lesion Segmentation
## 1 Introduction  
Skin cancer is one of the most common types of cancer. Excessive exposure to ultraviolet radiation can cause genetic mutations in superficial skin cells, leading to uncontrolled growth and the development of skin cancer. Melanoma, which arises from melanocyte cells, grows rapidly and poses a higher risk than non-melanoma skin cancer. Early treatment of patients can greatly reduce the mortality rate of melanoma [1]. In the early stages of skin disease detection, dermatologists conduct a detailed analysis of patient information by observing lesion regions and conducting biopsies of affected tissues. However, this segmentation approach is slow and susceptible to personal subjective bias, which can lead to significant structural changes after segmentation. In addition, because lesion regions often have low contrast in terms of surface hair, skin color, capillaries, shape, and texture compared to non-lesion areas, it is difficult for dermatologists to accurately assess the extent of lesions [2, 3]. Dermoscopy is a non-invasive detection technique that removes surface reflections from the skin to better visualize deeper layers of the skin. Dermoscopy uses magnification equipment to produce magnified images, so that clinicians can clearly see the originally invisible skin morphology and structure, and help them determine whether the pigmented skin lesions are melanoma or non-melanoma. Figure 1 shows some dermoscopy images. Accurate identification of skin lesions in medical images is one of the important research directions in modern medicine, and it is a key step in diagnosis and treating skin cancer [4]. The segmentation method of skin lesions is a difficult task because of significant variations in lesion size, irregular shape and boundaries, complex background information, and blurred edges. Skin lesion segmentation is a necessary step to identify melanoma. The skin lesion areas need to be accurately segmented from surrounding tissues so that doctors can fully observe them, further improving the accuracy of the diagnosis. With an increasing number of skin patients and a limited number of experienced dermatologists, computer-aided diagnosis methods have emerged as an effective way to solve these problems. Accurate segmentation of skin lesions with computer-aided diagnosis can help doctors quickly determine the location of the lesion. This not only significantly reduces the time and segmentation error cost of lesion image segmentation but also promotes further development of related technologies. Currently, classic segmentation algorithms typically perform pixel-level segmentation based on color, texture, and shape features in the image, such as the histogram thresholding method [5], unsupervised clustering method [6], edge-based and region-based method [7], and active contour method [8]. Although traditional skin lesion image segmentation algorithms can achieve a certain degree of segmentation effect, they overly rely on the quality of manually selected features and the introduction of prior information. Meanwhile, due to varying capturing conditions and inherent defects in images, noise interference such as hair, bubbles, and medical measurements may occur during dermoscopy imaging examination. Therefore, accurate extraction of lesions in dermoscopy images is still a significant problem. In the past few years, the rapid development of deep neural networks has disrupted traditional processing techniques, and deep neural networks have been used in people's lives. In the field of image segmentation, convolutional neural network models have achieved multiple breakthroughs, especially with the emergence of UNet [9], which has brought about a new wave of development in medical image segmentation. This is of great significance in assisting dermatologists to diagnose patients' diseases. UNet uses an encoder-decoder architecture to effectively avoid feature loss during downsampling by applying skip connections.