Efficient Wound Segmentation Using Lightweight U-Net with Adaptive Skip Fusion and Wound-Specific Attention Mechanisms

The main contributions of this work are summarized
as follows:
• Adaptive Skip Fusion (ASF), Wound-Specific Attention
(WSA), and Progressive Refinement (PR) are all inte-
grated into a single U-Net architecture in this lightweight
wound segmentation system.
• In order to retain computing efficiency while improving
the depiction of heterogeneous wound tissues and uneven
boundaries, a wound-oriented attention mechanism is
presented.
• With a Dice coefficient of 0.7584 and an improvement
in segmentation performance of 4.61% over the baseline
lightweight U-Net, the efficacy of the suggested frame-
work is demonstrated on a public wound segmentation
dataset.