Accurate image segmentation is the key to quantitative analysis and recognition of pathological tissues in medical imaging technology, which can provide important technical support for medical diagnosis and treatment. However, the task of lesion segmentation is particularly challenging due to the difficulty in identifying edges, the complexity of different tissues, and the variability in their shapes. To address these challenges, we propose a dual-channel compression mapping network (DCM-Net) with fused attention mechanism for medical image segmentation. Firstly, a dual-channel compression mapping module is added to U-Net's standard convolution blocks to capture inter-channel information. Secondly, we replace the traditional skip path with a fusion attention mechanism that can better present context information in high-level features. Finally, the combination of squeeze-and-excitation module and residual connection in the decoder part can improve the adaptive ability of the network. Through extensive experiments on various medical image datasets, DCM-Net has demonstrated superior performance compared to other models. For instance, on the ISIC database, our network achieved an Accuracy of 91.42%, True Positive Rate (TPR) of 88.93%, Dice of 86.09%, and Jaccard of 76.02%. Additionally, on the pituitary adenoma dataset from Quzhou People's Hospital, DCM-Net reached an Accuracy of 97.07%, TPR of 93.09%, Dice of 92.29%, and Jaccard of 87.73%. These results demonstrate the effectiveness of DCM-Net in providing accurate and reliable segmentation, and it shows valuable potential in the field of medical imaging technology.