Skin Melanoma Image Segmentation Algorithm Based on Improved YOLOv8

This study proposes a skin melanoma segmentation algorithm, YOLOv8-Skin, designed to address the issue of imprecise results in existing algorithms caused by diverse shapes and blurred edges. YOLOv8-Skin combines multiscale feature extraction and enhanced edge segmentation based on YOLOv8. First, the backbone network CSPDarkNet53 of YOLOv8 is replaced with U-Net v2, which is more suitable for medical image segmentation. This change introduces rich semantic information into low-level features and refines high-level features, enabling precise delineation of lesion boundaries and effective extraction of small structures in melanoma images. Second, a Deformable-Large Kernel Attention (D-LKA) mechanism is introduced into the neck's C2f, enhancing the model's ability to capture irregular image structures through deformable convolutions and improving multilevel feature fusion using large kernel convolutions. Finally, a Diverse Branch Block (DBB) is incorporated into the head, forming a new segmentation head that enhances the representation capability of single convolutions by combining diverse branches of different scales and complexities. This enriches the feature space and improves feature extraction. Experiments conducted on the ISIC2017, ISIC2018, and PH2 datasets verify the algorithm's effectiveness. On the ISIC2017 dataset, the Dice coefficient, Specificity, Sensitivity, and Accuracy reach 88.86%, 91.34%, 97.24%, and 96.29%, respectively. On the ISIC2018 dataset, they reach 91.64%, 95.42%, 96.69%, and 95.83%, respectively. On the PH2 dataset, they reach 95.92%, 95.43%, 97.02%, and 96.13%, respectively. The algorithm demonstrates stronger segmentation performance and is better suited for melanoma segmentation tasks compared to existing methods.