  Polyps segmentation poses a significant challenge in medical imaging due to
the flat surface of polyps and their texture similarity to surrounding tissues.
This similarity gives rise to difficulties in establishing a clear boundary
between polyps and the surrounding mucosa, leading to complications such as
local overexposure and the presence of bright spot reflections in imaging. To
counter this problem, we propose a new dual graph convolution network
(Dual-GCN) and location self-attention mechanisms with weighted fast
normalization fusion model, named DSFNet. First, we introduce a feature
enhancement block module based on Dual-GCN module to enhance local spatial and
structural information extraction with fine granularity. Second, we introduce a
location fused self-attention module to enhance the model's awareness and
capacity to capture global information. Finally, the weighted fast normalized
fusion method with trainable weights is introduced to efficiently integrate the
feature maps from encoder, bottleneck, and decoder, thus promoting information
transmission and facilitating the semantic consistency. Experimental results
show that the proposed model surpasses other state-of-the-art models in gold
standard indicators, such as Dice, MAE, and IoU. Both quantitative and
qualitative analysis indicate that the proposed model demonstrates exceptional
capability in polyps segmentation and has great potential clinical
significance. We have shared our code on anonymous website for evaluation.
