  Prompt treatment for melanoma is crucial. To assist physicians in identifying
lesion areas precisely in a quick manner, we propose a novel skin lesion
segmentation technique namely SLP-Net, an ultra-lightweight segmentation
network based on the spiking neural P(SNP) systems type mechanism. Most
existing convolutional neural networks achieve high segmentation accuracy while
neglecting the high hardware cost. SLP-Net, on the contrary, has a very small
number of parameters and a high computation speed. We design a lightweight
multi-scale feature extractor without the usual encoder-decoder structure.
Rather than a decoder, a feature adaptation module is designed to replace it
and implement multi-scale information decoding. Experiments at the ISIC2018
challenge demonstrate that the proposed model has the highest Acc and DSC among
the state-of-the-art methods, while experiments on the PH2 dataset also
demonstrate a favorable generalization ability. Finally, we compare the
computational complexity as well as the computational speed of the models in
experiments, where SLP-Net has the highest overall superiority
