SkinSegNet : An Advanced Encoder–Decoder for Skin Lesion Segmentation Using Channel‐Aware and Spatial Cross‐Scale Attention

ABSTRACT To achieve precise and efficient skin cancer segmentation, an innovative SkinSegNet architecture is proposed. Inspired by U‐Net, SkinSegNet uses an encoder–decoder architecture incorporating advanced feature extraction and attention mechanisms. The encoder utilizes convolutional blocks and pooling attention (PA) layers to downsample feature maps and focus on significant regions. At the bottleneck, the proposed feature aggregation block integrates channel‐aware multihead attention (CAMA) and Adaptive Spatial Cross‐Scale Attention (ASCA) modules. These modules let the model capture channel relationships and complex spatial dependencies for accurate segmentation. The decoder reconstructs the segmentation mask through continuous upsampling and skips connections, ensuring fine‐grained spatial detail is preserved. Experiments are carried out on benchmark datasets namely ISIC 2016, ISIC 2017, and ISIC 2018 demonstrating SkinSegNet's superior performance in skin lesion segmentation, achieving state‐of‐the‐art accuracy of 95.12%, 94.01%, and 95.04%, respectively. Furthermore, cross‐dataset experiments show that SkinSegNet performs well on ISIC datasets, demonstrating its strong generalization ability.