Convolutional neural networks (CNNs) have been widely used in the field of medical image analysis. However, due to the limitation of its fixed receptive field, the traditional CNN model makes it difficult to establish long-distance dependencies in images. Transformer can establish the information dependence in the global perspective of the image through the self-attention mechanism and has stronger sequence modeling ability. However, Transformer makes it difficult to capture the local detailed features of images. To solve the above problems, a hybrid model DC-TransNet based on the CNN and Transformer is proposed for medical image segmentation. DC-TransNet uses a dual-decoder structure to establish local and long-distance dependencies in the image and capture local and global features. Considering that the size of the feature maps extracted by the network model based on the encoder-decoder structure is inconsistent at different depths, we design two feature perception attention mechanisms, channel feature percep-tion attention (CFP) and spatial feature perception attention (SFP), to reasonably allocate the weight of local and global features. Experiments are conducted on multiple medical datasets and the results show that DC-TransNet is effective in 2D medical images. Competitive results are achieved in single-category segmentation tasks, and coefficients such as mIoU and mDice are significantly improved.