ing at the problem of skin lesion image segmentation, a skin lesion image segmentation is proposed based on multi-scale DenseNet. First, the morphological closing operation and the un-sharp filter are used to preprocess the original skin lesion image and to obtain a refinement image without hairs and blood-vessel artifacts. Then, the pre-processed image is input into a segmentation network. This network is based on an encoder-decoder architecture and uses two multi-scale feature fusion methods of parallel multi-branch structure and pyramid pooling block model to achieve feature extraction under different receptive fields. Furthermore, the DenseNet structure is integrated into the encoder to realize the reuse of image features, and the L_Total loss function which combines target loss and content loss is adopted to further improve the accuracy of image segmentation. Finally, the segmentation results are obtained through the SoftMax classifier and the related evaluation indicators are calculated. The experimental results on the ISBI 2016 skin lesion image dataset show that the accuracy, Dice coefficient, Jaccard index, sensitivity, and specificity are 95.48%, 96.37%, 93.41%, 92.93%, and 96.49%, respectively, and the whole performance here is better than those of the existing algorithms. The proposed algorithm can accurately segment skin lesions and thus it can be applied to the melanoma computer-aided diagnosis systems.