Research on melanoma image segmentation method based on improved SwinUNet

Aiming at the problems of fuzzy boundary and poor segmentation effect of SwinUNet in melanoma image segmentation, an improved SwinUNet network segmentation method was proposed. Firstly, Dice loss function is used to alleviate the background and regional imbalance. Secondly, each decoder layer is made to fuse the smaller scale from the encoder, the same scale feature map and the larger scale feature map from the decoder, so that the fine-grained semantics and coarse-grained semantics at the full scale can be captured . Finally, the size of the sliding window is increased, the receptive field of the model is enlarged, and the Dice coefficient is used to evaluate the segmentation results. The average Dice values of the original SwinUNet and the three improved models were 0.8311, 0.8689, 0.8719 and 0.8661, respectively. The experimental results show that the improved model proposed in this paper can effectively improve the accuracy of the original model, which is extremely important for the early diagnosis and treatment of melanoma.