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Prompt treatment for melanoma is crucial. To assist physicians in identifyinglesion areas precisely in a quick manner, we propose a novel skin lesionsegmentation technique namely SLP-Net, an ultra-lightweight segmentationnetwork based on the spiking neural P(SNP) systems type mechanism. Mostexisting convolutional neural networks achieve high segmentation accuracy whileneglecting the high hardware cost. SLP-Net, on the contrary, has a very smallnumber of parameters and a high computation speed. We design a lightweightmulti-scale feature extractor without the usual encoder-decoder structure.Rather than a decoder, a feature adaptation module is designed to replace itand implement multi-scale information decoding. Experiments at the ISIC2018challenge demonstrate that the proposed model has the highest Acc and DSC amongthe state-of-the-art methods, while experiments on the PH2 dataset alsodemonstrate a favorable generalization ability. Finally, we compare thecomputational complexity as well as the computational speed of the models inexperiments, where SLP-Net has the highest overall superiority

SLP-Net:An efficient lightweight network for segmentation of skin lesions