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arXiv · 2312.12789

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

Bo Yang·Hong Peng·Chenggang Guo·Xiaohui Luo·Jun Wang·Xianzhong Long
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Abstract

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

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Bo Yang, Hong Peng, Chenggang Guo, Xiaohui Luo, Jun Wang, Xianzhong Long. 2023-12-20. SLP-Net:An efficient lightweight network for segmentation of skin lesions. https://arxiv.org/abs/2312.12789

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