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Open AccessArticle
Skin Lesion Segmentation through Generative Adversarial Networks with Global and Local Semantic Feature Awareness
by
Ruyao Zou
Ruyao Zou ,
Jiahao Zhang
Jiahao Zhang and
Yongfei Wu
Yongfei Wu *
College of Artificial Intelligence, Taiyuan University of Technology, Jinzhong 030600, China
*
Author to whom correspondence should be addressed.
Electronics 2024, 13(19), 3853; https://doi.org/10.3390/electronics13193853 (registering DOI)
Submission received: 18 July 2024
/
Revised: 4 September 2024
/
Accepted: 25 September 2024
/
Published: 28 September 2024
Abstract
The accurate segmentation of skin lesions plays an important role in the diagnosis and treatment of skin cancers. However, skin lesion areas are rich in details and local features, including the appearance, size, shape, texture, etc., which pose challenges for the accurate localization and segmentation of the target area. Unfortunately, the consecutive pooling and stride convolutional operations in existing convolutional neural network (CNN)-based solutions lead to the loss of some spatial information and thus constrain the accuracy of lesion region segmentation. In addition, using only the traditional loss function in CNN cannot ensure that the model is adequately trained. In this study, a generative adversarial network is proposed, with global and local semantic feature awareness (GLSFA-GAN) for skin lesion segmentation based on adversarial training. Specifically, in the generator, a multi-scale localized feature fusion module and an effective channel-attention module are designed to acquire the multi-scale local detailed information of the skin lesion area. In addition, a global context extraction module in the bottleneck between the encoder and decoder of the generator is used to capture more global semantic features and spatial information about the lesion. After that, we use an adversarial training strategy to make the discriminator discern the generated labels and the segmentation prediction maps, which assists the generator in yielding more accurate segmentation maps. Our proposed model was trained and validated on three public skin lesion challenge datasets involving the ISIC2017, ISIC2018, and HAM10000, and the experimental results confirm that our proposed method provides a superior segmentation performance and outperforms several comparative methods.
Share and Cite
MDPI and ACS Style
Zou, R.; Zhang, J.; Wu, Y.
Skin Lesion Segmentation through Generative Adversarial Networks with Global and Local Semantic Feature Awareness. Electronics 2024, 13, 3853.
https://doi.org/10.3390/electronics13193853
AMA Style
Zou R, Zhang J, Wu Y.
Skin Lesion Segmentation through Generative Adversarial Networks with Global and Local Semantic Feature Awareness. Electronics. 2024; 13(19):3853.
https://doi.org/10.3390/electronics13193853
Chicago/Turabian Style
Zou, Ruyao, Jiahao Zhang, and Yongfei Wu.
2024. "Skin Lesion Segmentation through Generative Adversarial Networks with Global and Local Semantic Feature Awareness" Electronics 13, no. 19: 3853.
https://doi.org/10.3390/electronics13193853
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