Research on Colon Polyp Image Segmentation Algorithm Based on Feature Interactive Fusion
A feature interactive fusion segmentation network called FIFNet is proposed in this paper to address the issue of low segmentation accuracy caused by the variable scale of the lesion area in colon polyp images and the difficulty of capturing its internal complex relationships of traditional methods.In the network,it utilizes Pyramid Vision Transformer(PVT)and ResNet18 to extract local and global features of polyp images in parallel,and fuses the semantic information between the two by the Semantic Harmonization(SH)unit.Then,it designs the Inter-layer Attention(IA)aggregation module to highlight the morphological and texture information of polyp images through adaptive weighted fusion of different layer features.Finally,the Inverse Residual Attention(IRA)module fully explores the connection between the polyp area and boundary to improve the accuracy of segmentation results.Experimental tests are conducted on the public datasets Kvasir,CVC-ClinicDB,CVC-ColonDB,ETIS and Endosece where the mDice coefficients are 0.929,0.941,0.821,0.794,and 0.900,respectively.Experimental results show that the FIFNet network has a certain application value in polyp image segmentation.
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