Research on deep learning image segmentation method based on attention mechanism

In this paper, combining the feature extraction advantages of deep learning technology in image processing, we add the attention mechanism in deep convolutional neural network with a large convolutional kernel as the core of the module. And the multi-scale fusion module is designed to effectively fuse the large convolutional kernel CNN branch and the augmented Transformer branch to form an image segmentation method based on the large convolutional kernel and attention mechanism feature fusion network (TCNet). Setting up the experimental environment, the sample images were subjected to data enhancement operations of vertical flip, horizontal flip, diagonal flip, random shift and random scale change, and the parameters of accuracy (AC), sensitivity (SE), specificity (SP), Jaccard’s coefficient (JA), and Dice’s coefficient (DI) were selected as the evaluation indexes, and the model performance testing was carried out by using several classical datasets. The TCNet model proposed in this paper achieves 90.334% JA, and the indexes of pixel-AC and DI are 93.457% and 91.773%, respectively, and the model has better segmentation performance. In this paper, the TCNet model is designed to achieve the extraction of global context information and local detail information of the image by using the multiple attention mechanism, which improves the image segmentation performance and has a wider scope of application.