1(School of Life Health Information Science and Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China) 2(School of Optoelectronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China)
Abstract：Accurate segmentation of skin lesion areas is essential for computer-aided diagnosis. However, irregular shapes, blurred boundaries and noise interference in skin lesion images lead to significant challenges to achieving high segmentation accuracy. To address these difficulties, we proposed an improved dense recurrent residual U-Net model (IDR2U-Net) for precise skin lesion area segmentation. Firstly, we optimized the original convolutional blocks in the encoding and decoding layers into recurrent residual convolution modules using dense connections to mitigate gradient vanishing problems in deep networks. Secondly, we introduced feature adaptation modules to strengthen the fusion of adjacent features by suppressing irrelevant background noise and amplifying informative features. Additionally, a dual attention mechanism was designed to enhance global information utilization efficiency using spatial attention and improve the correlation of channel features using channel attention, increasing network precision in skin lesion area segmentation. Furthermore, we mitigated class imbalance in dermoscopic image segmentation through joint Dice coefficient and cross-entropy loss function in our network training. Finally, ablation and comparative experiments were conducted using over 2 000 images from the ISIC 2017 skin lesion dataset. The IDR2U-Net model achieved Jaccard, Dice, and accuracy scores of 78.86%, 86.92%, and 94.61%, respectively. The experimental results demonstrated that the proposed IDR2U-Ne not only enhanced the accuracy but also achieved finer image segmentation, particularly in handling blurred boundary images, where it effectively reduced under-segmentation.
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