Skin lesion segmentation methods are capable of autonomously segmenting the lesion area, thereby providing precise support for lesion diagnosis and treatment. In dermoscopic images, global contextual information facilitates the prediction of the lesion’s central area, while local detail information aids in predicting the lesion’s boundary. Nevertheless, the majority of existing networks don’t fully exploit these two types of features. Consequently, this paper proposes a Mixed Feature Perception Network (MFPNet) that effectively integrates global contextual features and local features to proficiently segment the lesion area. Additionally, we devised a Multi-scale Feature Perception Module (MFPM) that mitigates the loss of spatial information during downsampling, thereby assisting the backbone in reconstructing mixed features. Ultimately, we employ an Adaptive Decoder (AD) to amalgamate the feature transmitted by the encoder and the upper-level decoder to enhance the network’s robustness. Experiments demonstrate that MFPNet surpasses other state-of-the-art methods on both the ISIC2018 dataset and the PH \(^{2}\) dataset. Our code is open-source and available at https://github.com/XYQ1517/MFPNet.