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

Abstract

Most existing convolutional neural networks achieve high segmentation accuracy while neglecting the high hardware cost. In this paper, we integrate Spiking neural P-type(SNP-type) and depthwise convolution to introduce a multi-channel SNP-type convolution (MCConvSNP). With it, we propose a lightweight segmentation neural network to assist physicians in precisely identifying lesion areas. The proposed network is an asymmetric network with only encoders, and the decoder is complemented by adaptive fusion and skip connections. It is an asymmetric design that reduces a large number of network parameters. Furthermore, the encoder is composed of an MCConvSNP pyramid, which has a small parameter and enables fast multi-scale feature information extraction. Experiments at ISIC2018 dataset challenge demonstrate that the proposed model has the highest Acc and DSC among the state-of-the-art methods. At the same time, generalization experiments on ISIC2016 dataset and 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.