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

• URL: http://arxiv.org/abs/2312.12789v2
• Date: Thu, 4 Jan 2024 09:34:08 GMT
• Title: SLP-Net:An efficient lightweight network for segmentation of skin lesions
• Authors: Bo Yang, Hong Peng, Chenggang Guo, Xiaohui Luo, Jun Wang, Xianzhong Long
• Abstract summary: SLP-Net is an ultra-lightweight skin lesion segmentation network based on the spiking neural P(SNP) systems type mechanism. We design a lightweight multi-scale feature extractor without the usual encoder-decoder structure. Experiments at the ISIC2018 challenge demonstrate that the proposed model has the highest Acc and DSC among the state-of-the-art methods.
• Score: 9.812172372998358
• License: http://creativecommons.org/licenses/by/4.0/
• 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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