To address the problems of skin lesions, such as varied sizes, low contrast with surrounding skin, blurred and irregular boundaries, artifacts, and hair interference, this study proposes a skin lesion segmentation algorithm that combines edge enhancement with multi-scale information fusion. The algorithm consists of an encoder, a multi-scale sensing module, an edge enhancement module, and a lightweight decoder. Firstly, a Transformer module is built in the encoder to extract global information, and convolution operations are used to extract local information. Secondly, a multi-scale sensing module is designed to integrate multi-scale features using a gated atrous convolution pyramid module with a dense connection structure. An edge enhancement module is constructed, utilizing deep features to promote the exploration of edge features to better retain details and edge information. Finally, a lightweight decoder is designed, employing the CARAFE lightweight operator for upsampling, to maintain high segmentation accuracy with fewer parameters. Comparative experiments on open data sets ISIC2016 and ISIC2018 show that the segmentation accuracy of the proposed algorithm is higher than that of other popular algorithms.