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  • Open Access


    SepFE: Separable Fusion Enhanced Network for Retinal Vessel Segmentation

    Yun Wu1, Ge Jiao1,2,*, Jiahao Liu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2465-2485, 2023, DOI:10.32604/cmes.2023.026189

    Abstract The accurate and automatic segmentation of retinal vessels from fundus images is critical for the early diagnosis and prevention of many eye diseases, such as diabetic retinopathy (DR). Existing retinal vessel segmentation approaches based on convolutional neural networks (CNNs) have achieved remarkable effectiveness. Here, we extend a retinal vessel segmentation model with low complexity and high performance based on U-Net, which is one of the most popular architectures. In view of the excellent work of depth-wise separable convolution, we introduce it to replace the standard convolutional layer. The complexity of the proposed model is reduced by decreasing the number of… More >

  • Open Access


    Sea-Land Segmentation of Remote Sensing Images Based on SDW-UNet

    Tianyu Liu1,3,4, Pengyu Liu1,2,3,4,*, Xiaowei Jia5, Shanji Chen2, Ying Ma2, Qian Gao1,3,4

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1033-1045, 2023, DOI:10.32604/csse.2023.028225

    Abstract Image segmentation of sea-land remote sensing images is of great importance for downstream applications including shoreline extraction, the monitoring of near-shore marine environment, and near-shore target recognition. To mitigate large number of parameters and improve the segmentation accuracy, we propose a new Squeeze-Depth-Wise UNet (SDW-UNet) deep learning model for sea-land remote sensing image segmentation. The proposed SDW-UNet model leverages the squeeze-excitation and depth-wise separable convolution to construct new convolution modules, which enhance the model capacity in combining multiple channels and reduces the model parameters. We further explore the effect of position-encoded information in NLP (Natural Language Processing) domain on sea-land… More >

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