Deepskinformer: Skin Lesion Segmentation Using Hierarchical Transformers And Edge Enhancement

Segmentation of skin lesions from dermatological images is critical in diagnosing and treating skin cancer. Despite this, the diversity of lesion shapes, sizes, and textures against a similar-toned skin backdrop makes these images challenging to analyze. Current segmentation methods are often less precise in delineating boundaries and more susceptible to interference from background noise. To address this issue, we introduce an end-to-end framework called DeepSkinFormer (DSF) for skin lesion segmentation using the Skin Edge Enhancement Module (SEEM) to enhance boundaries for efficient detection. We evaluate the proposed model on standard benchmarks, HAM10000, ISIC2017, and PH2 datasets. Our model outperforms existing methods and achieves stateof-the-art results using the Dice and mean Intersection Over Union (mIOU) scores. Furthermore, we conduct an ablation study to confirm the significant contributions of DSFspecialized modules to their effectiveness.