Multi-Scale Feature Integration and Spatial Attention for Accurate Lesion Segmentation

This paper proposes an enhanced feature pyramid-based segmentation algorithm designed for accurate skin disease lesion segmentation. The method utilizes a multi-level convolutional encoder to extract hierarchical features and introduces a cross-scale enhancement module to strengthen the fusion of multi-scale contextual information. Additionally, a space attention mechanism is applied to refine spatial localization and highlight critical lesion regions. The network architecture is systematically constructed to balance the extraction of fine-grained details and global semantic representations. Extensive experiments on the ISIC 2018 dataset demonstrate that the proposed method achieves superior performance compared to several baseline models, showing notable improvements in mIOU, mDice, and F1-Score metrics. Intuitive visualization of segmentation masks and feature maps further validates the model's ability to accurately capture lesion boundaries and suppress background noise. The effectiveness of the enhanced feature pyramid structure confirms its potential to advance the precision and robustness of skin disease image analysis. Through comprehensive evaluations, the method is shown to offer a reliable and scalable solution for skin lesion segmentation tasks.