Parallel Correlation Attention Modules are Used for Feature Extraction and Fusion to Achieve Accurate Target Segmentation

The conventional convolutional neural network (CNN) model has limitations in terms of global modeling ability, as it can only extract local features and is susceptible to noise interference. Due to repeated downsampling, small target features are easily lost in the deeper layers of the network. On the other hand, Transformer models are renowned for their exceptional global modeling capabilities; however, for the specific task of polyp images, effective results necessitate local feature extraction. Therefore, when reconsidering the relationship between local and global aspects within CNN and self-attention models, we propose a parallel relevant attention module with a Transformer structure for feature extraction and fusion. By combining channel attention and spatial attention mechanisms, we obtain semantic information from the lowest-level features to determine target feature locations accurately. Additionally, our step-by-step feature fusion module integrates shallow feature information into deeper layers through a CNN structure to capture more detailed target features comprehensively. Finally, these detailed target features are combined with underlying semantic information to achieve precise target segmentation.