Combining global gate axial-attention with U-Net for skin lesion segmentation

Shaojie Zhu; Long Ma

doi:10.1117/12.3034995

3 September 2024 Combining global gate axial-attention with U-Net for skin lesion segmentation

Shaojie Zhu, Long Ma

Proceedings Volume 13227, 2024 AI Photonics Technology Symposium; 1322703 (2024) https://doi.org/10.1117/12.3034995
Event: 2024 AI Photonics Technology Symposium, 2024, Wuhan, China

Abstract

In dermatology, the mortality rate for melanoma is as high as 1.62%, imposing a significant burden on patients. Diagnosis primarily relies on the experience of physicians, who examine the skin’s color and texture. Traditional computer-assisted lesion segmentation has a low accuracy. However, with advancements in deep learning and the integration of various factors from medical imaging, the accuracy of lesion segmentation can be enhanced. Our skin disease detection method, based on deep learning, uses the GGAD-Net model and the SeDice loss function, with a weighting parameter of 0.5 for SeDice. On the ISIC 2018 dataset, after 50 epochs of training, the GGAD-Net model achieved an accuracy of 92.2%, a recall rate of 96%, a Dice score of 92.3%, and an IoU of 88.4%. The results prove that this model can effectively improve the performance of lesion segmentation, and in the future, we aim to further enhance accuracy and expand to other medical imaging segmentation applications.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Shaojie Zhu and Long Ma "Combining global gate axial-attention with U-Net for skin lesion segmentation", Proc. SPIE 13227, 2024 AI Photonics Technology Symposium, 1322703 (3 September 2024); https://doi.org/10.1117/12.3034995

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KEYWORDS

Image segmentation

Skin

Medical imaging

Matrices

Machine learning

Deep learning

Education and training

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