Optimizing Skin Lesion Segmentation With Robust Regularization and Boundary-Aware Techniques.

Skin segmentation of lesions can be challenging because it relies on numerous factors such as size, shape, and position. Convolutional neural networks (CNN) have shown exceptional efficacy in automating image segmentation. The inability of current CNNs to maintain local features and emphasize pertinent information restricts decision-making ability. To achieve more precise medical image segmentation, this research suggests a CNN with a spatial feature mechanism (UCMNet). The UNet architecture serves as the foundation for UCMNet. To retain an image's local features during downsampling and increase the relevance of the feature maps supplied to the upsampling module relative to the ground truth, a spatial multiple-scale characteristic (SA) module is included in the decoder. Comprehensive tests were conducted on the 3D-TBP dataset. The suggested UCMNet outperformed UNet in skin lesion tissue segmentation, with an average Dice score improvement of 0.82% and 0.85%, respectively. The suggested UCMNet outperformed UNet in skin lesion tissue segmentation, with an average Dice score improvement of 0.03 and the Intersection over Union also rose by 0.08. The pixel error fell from 0.7132 to 0.6083 pixels, indicating better segmentation performance. When the segmentation area contains interference factors and the initial image of skin lesions has an indistinct boundary, UCMNet can achieve better results. It demonstrated strong segmentation ability by incorporating attention mechanisms into the encoder and applying comprehensive attention methods.