Improved Dense Recurrent Residual U-Net for Skin Lesion Segmentation

Yang Yuan; Dechun Zhao; Zixin Luo

doi:10.23977/jeis.2024.090105

Improved Dense Recurrent Residual U-Net for Skin Lesion Segmentation

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DOI: 10.23977/jeis.2024.090105 | Downloads: 15 | Views: 256

Author(s)

Yang Yuan ¹, Dechun Zhao ¹, Zixin Luo ¹

Affiliation(s)

¹ College of Bioinformation, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

Corresponding Author

Yang Yuan

ABSTRACT

Accurate segmentation of skin lesion areas is of great significance for computer-aided diagnosis. However, due to the irregular shape, boundary blurring, and noise interference of skin lesion images, accurate segmentation is difficult and has low precision. Therefore, it proposes an improved dense recurrent residual U-Net model. Firstly, This improved network use of dense recurrent residual connections in the Squeeze-and-Excitation convolution block design to alleviate gradient vanishing and provide accurate location information for segmentation; Secondly, the integration of feature adaptive modules between the encoder and decoder to enhance feature fusion between adjacent layers. Finally, a combined Dice and cross-entropy loss function is adopted to mitigate the class imbalance issue in skin lesion image segmentation. The model is evaluated on the public dataset ISIC 2017, achieving Jaccard, Dice, and accuracy scores of 78.86%, 86.92%, and 94.61% respectively. The experimental results demonstrate that the proposed model outperforms other networks in terms of segmentation performance and provides more accurate segmentation results.

KEYWORDS

Medical image processing, segmentation of skin lesion, U-Net, Squeeze-and-Excitation block, feature adaptation module

CITE THIS PAPER

Yang Yuan, Dechun Zhao, Zixin Luo, Improved Dense Recurrent Residual U-Net for Skin Lesion Segmentation. Journal of Electronics and Information Science (2024) Vol. 9: 27-37. DOI: http://dx.doi.org/10.23977/10.23977/jeis.2024.090105.

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Improved Dense Recurrent Residual U-Net for Skin Lesion Segmentation

Author(s)

Affiliation(s)

Corresponding Author

ABSTRACT

KEYWORDS

CITE THIS PAPER

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