Colonic Polyp Segmentation Algorithm Based on Improved SegFormer Network

To solve the problem that factors such as the similar color of polyps and background in colon polyp images and the different sizes of polyps affect the segmentation accuracy, an improved SegFormer, U-SegFormer, is proposed for colon polyp image segmentation. Firstly, in the feature fusion phase of the decoder, features at multiple scales from the encoder output are fused in a cascade fashion and the feature representation is enhanced using the Unified Attention Fusion Module; Then, training the network in combination with transfer learning, using a loss function combining Dice Loss and Focal Loss to mitigate the effect of positive and negative sample imbalance on model training. Finally, the U-SegFormer was compared with other segmentation models, and the experimental results showed that the polyp segmentation method based on the U-SegFormer model was superior to the current mainstream segmentation methods and had a certain potential for clinical application.