###### Abstract
Usually, it is the borders of the melanoma which are uneven and may contain rough edges, which are imprecisely defined. Hence, skin segmentation is performed at an early stage to get the border information or regions of interest (ROI). Such approaches have been proven to be beneficial for the subsequent classification or detection task [11; 12]. It is a challenging task to perform automatic skin lesion segmentation on some skin lesions where lesions are not that clear or have a light pigment. It gets complicated when colour or visual patterns are similar, and the boundaries are alike for different melanomas. In such cases, skin segmentation becomes enormously difficult. It gets more complicated for computers to read images directly which are of high resolution, and resource intensive. Having such properties make the process slow. To expedite the process, the image size is reduced using down-sampling, which in turn has a negative effect on the textures and subtlety. Due to that, it gets more complicated to differentiate the boundaries of these skin lesions. Moreover, the colour and texture distribution gets affected by some elements present in skin lesions such as blood vessels, colour illumination and hair artefacts, which have a negative impact on learning. Figure 1 shows some sample images of the ISIC dataset [13]. ISIC contains the digital skin images of melanoma. The aim of keeping such images publicly available is to educate the public/professionals about melanoma, examine them and provide some diagnoses through teledermatology, clinical decision support and automated diagnosis. Applying skin lesion segmentation on such a dataset is a challenging task due to the characteristics of these images. As it can be seen from the figure some images are covered with hair, some lesions have blood vessels around them and some lesion images have fuzzy boundaries, which makes skin lesion segmentation difficult. Different approaches have been proposed for skin lesion segmentation by incorporating different CNN architectures with multi-scale information [12; 14; 15] and multi-task learning frameworks [16; 17] with auxiliary information [18]. The aim of these proposed approaches is to utilize as much data available to detect skin lesions and make the right diagnosis. Nevertheless, such methods either require extra labelling or use extensive parameters which are impractical in real situations. Although CNNs have achieved the best results on various vision tasks, they have still shown limited performance on skin lesion segmentation tasks due to a lack of understanding of long-range spatial relations in skin lesion images. In order to overcome this limitation, the recently proposed Vision Transformer (ViT) [19] for image classification employs a purely self-attention-based model that learns long-range spatial relations to focus on the image's relevant parts. To achieve better performance, existing transformer-based network architectures require large-scale datasets. However, because medical imaging datasets are small, applying pure transformers to medical image analysis is difficult. ViT emphasizes low-resolution features, claiming that the successive downsamplings result in a lack of detailed localization information, rendering it unsuitable for skin lesion image classification. To improve the recovery of detailed localization information, several ViT-based image segmentation methods have recently been combined with ConvNets in the natural image domain. Furthermore, different architecture called UNet [20], based on CNN with few changes in its architecture, has been developed and incorporated into biomedical images not only to just classify the disease but also to identify the area of infection. Due to its success rate on medical images, many researchers [21; 22; 23; 24] have adopted this model and use it for skin segmentation. This study provides a combined architecture that combines ViT and ConvNet to provide an efficient skin lesion image segmentation, which will assist in the diagnosis of skin lesions.