Soni Tanishq, Gupta Sheifali, Almogren Ahmad, Altameem Ayman, Rehman Ateeq Ur, Hussen Seada, Bharany Salil
Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India.
Department of Computer Science, College of Computer and Information Sciences, King Saud University, 11633, Riyadh, Saudi Arabia.
Sci Rep. 2025 Mar 18;15(1):9262. doi: 10.1038/s41598-025-94380-9.
Skin lesion segmentation presents significant challenges due to the high variability in lesion size, shape, color, and texture and the presence of artifacts like hair, shadows, and reflections, which complicate accurate boundary delineation. To address these challenges, we proposed ARCUNet, a semantic segmentation model including residual convolutions and attention techniques to improve segmentation accuracy to address the challenges of skin lesion segmentation, By incorporating residual convolutions and attention mechanisms, ARCUNet enhances feature learning, stabilizes training, and sharpens focus on lesion boundaries for improved segmentation accuracy. Residual convolutions ensure better gradient flow and faster convergence, while attention mechanisms refine feature selection by emphasizing critical lesion regions and suppressing irrelevant details. The model was tested on the ISIC 2016, 2017, and 2018 datasets with outstanding segmentation results with accuracy measures of 98.12%, 96.45%, and 98.19%, Dice measures of 94.68%, 91.21%, and 95.34%, and Jaccard measures of 91.14%, 88.33%, and 93.53%, respectively. These findings signify the ability of ARCUNet to segment skin lesions accurately and thus as an effective tool for computerized skin disease diagnosis.
由于皮肤病变在大小、形状、颜色和纹理方面存在高度变异性，并且存在毛发、阴影和反射等伪影，这些都使得准确勾勒边界变得复杂，因此皮肤病变分割面临重大挑战。为应对这些挑战，我们提出了ARCUNet，这是一种语义分割模型，包括残差卷积和注意力技术，以提高分割精度，从而应对皮肤病变分割的挑战。通过结合残差卷积和注意力机制，ARCUNet增强了特征学习，稳定了训练，并更清晰地聚焦于病变边界以提高分割精度。残差卷积确保了更好的梯度流和更快的收敛，而注意力机制通过强调关键病变区域和抑制无关细节来优化特征选择。该模型在ISIC 2016、2017和2018数据集上进行了测试，分割结果出色，准确率分别为98.12%、96.45%和98.19%，Dice系数分别为94.68%、91.21%和95.34%，Jaccard系数分别为91.14%、88.33%和93.53%。这些发现表明ARCUNet能够准确分割皮肤病变，因此是一种用于计算机化皮肤病诊断的有效工具。
