基于M2SNet的结直肠息肉图像分割OA

Accurate medical image segmentation is of great value for clinical diagnosis.However,lesions often present irregular shapes,blurred edges and other features in medical images,posing great challenges to rapid screening and accurate diagnosis.To this end,this study proposes a network model M2SNet based on the multi-scale subtraction mechanism and applies it to polyp detection in medical images.On five polyp datasets,it conducts comparative experiments with mainstream models such as U-Net,Attention-UNet,TransUNet,ResUNet,and so on.The results show that M2SNet shows better segmentation performance.The core innovation of M2SNet is to effectively filter out feature redundancy through multi-scale subtraction units,strengthen the complementarity between different levels of features,and then achieve accurate positioning and clear boundary segmentation of lesions.Compared with the traditional U-Net,this model significantly improves the ability to depict edge details while maintaining structural simplicity.