Axial-FNet: skin cancer image segmentation model based on fuzzy convolution combined with gated axial self-attention
The task of skin cancer image segmentation is a key task in the field of medical image processing. The commonly used segmentation algorithms can't well balance the computational resource requirements of local information and global context information when performing diagnosis. In addition, the...
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| Format: | Article |
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POSTS&TELECOM PRESS Co., LTD
2025-06-01
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| Series: | 智能科学与技术学报 |
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| Online Access: | http://www.cjist.com.cn/zh/article/doi/10.11959/j.issn.2096-6652.202520/ |
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| _version_ | 1849409200036249600 |
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| author | JIANG Shu CHEN Kun DING Weiping ZHOU Tianyi ZHU Yue |
| author_facet | JIANG Shu CHEN Kun DING Weiping ZHOU Tianyi ZHU Yue |
| author_sort | JIANG Shu |
| collection | DOAJ |
| description | The task of skin cancer image segmentation is a key task in the field of medical image processing. The commonly used segmentation algorithms can't well balance the computational resource requirements of local information and global context information when performing diagnosis. In addition, the problem of fuzzy tumor boundaries and difficulty in correctly identifying segmentation is also an urgent problem to be solved. Aiming at the above problems, a skin cancer image segmentation model Axial-FNet based on fuzzy convolution combined with gated axial self-attention was proposed. The model was composed of a gated axial self-attention branch and a fuzzy convolutional neural network branch. At the end of the gated axial self-attention branch, a gated weight controller was set to control the proportion and degree of capturing local information and global context information. The fuzzy learning module was fused into the convolutional neural network (CNN) to form a fuzzy neural network branch to extract the local information of the image. The segmentation accuracy was improved by the model while reducing the amount of calculation. The performance of the Axial-FNet model was evaluated on the ISIC 2017 dataset, achieving scores of 74.23%, 83.05%, and 92.89% for MIoU, F1-score, and accuracy, respectively, as well as 80.91%, 88.13%, and 93.10% for the same metrics on the ISIC 2018 dataset. The experimental results show that Axial-FNet has better segmentation accuracy and reliability than other advanced segmentation models. |
| format | Article |
| id | doaj-art-48c6830dce304ce38f95a836906a2043 |
| institution | Kabale University |
| issn | 2096-6652 |
| language | zho |
| publishDate | 2025-06-01 |
| publisher | POSTS&TELECOM PRESS Co., LTD |
| record_format | Article |
| series | 智能科学与技术学报 |
| spelling | doaj-art-48c6830dce304ce38f95a836906a20432025-08-20T03:35:34ZzhoPOSTS&TELECOM PRESS Co., LTD智能科学与技术学报2096-66522025-06-017221233117464464Axial-FNet: skin cancer image segmentation model based on fuzzy convolution combined with gated axial self-attentionJIANG ShuCHEN KunDING WeipingZHOU TianyiZHU YueThe task of skin cancer image segmentation is a key task in the field of medical image processing. The commonly used segmentation algorithms can't well balance the computational resource requirements of local information and global context information when performing diagnosis. In addition, the problem of fuzzy tumor boundaries and difficulty in correctly identifying segmentation is also an urgent problem to be solved. Aiming at the above problems, a skin cancer image segmentation model Axial-FNet based on fuzzy convolution combined with gated axial self-attention was proposed. The model was composed of a gated axial self-attention branch and a fuzzy convolutional neural network branch. At the end of the gated axial self-attention branch, a gated weight controller was set to control the proportion and degree of capturing local information and global context information. The fuzzy learning module was fused into the convolutional neural network (CNN) to form a fuzzy neural network branch to extract the local information of the image. The segmentation accuracy was improved by the model while reducing the amount of calculation. The performance of the Axial-FNet model was evaluated on the ISIC 2017 dataset, achieving scores of 74.23%, 83.05%, and 92.89% for MIoU, F1-score, and accuracy, respectively, as well as 80.91%, 88.13%, and 93.10% for the same metrics on the ISIC 2018 dataset. The experimental results show that Axial-FNet has better segmentation accuracy and reliability than other advanced segmentation models.http://www.cjist.com.cn/zh/article/doi/10.11959/j.issn.2096-6652.202520/image segmentationfuzzy learninggated axial self-attentiongating mechanismconvolutional neural network |
| spellingShingle | JIANG Shu CHEN Kun DING Weiping ZHOU Tianyi ZHU Yue Axial-FNet: skin cancer image segmentation model based on fuzzy convolution combined with gated axial self-attention 智能科学与技术学报 image segmentation fuzzy learning gated axial self-attention gating mechanism convolutional neural network |
| title | Axial-FNet: skin cancer image segmentation model based on fuzzy convolution combined with gated axial self-attention |
| title_full | Axial-FNet: skin cancer image segmentation model based on fuzzy convolution combined with gated axial self-attention |
| title_fullStr | Axial-FNet: skin cancer image segmentation model based on fuzzy convolution combined with gated axial self-attention |
| title_full_unstemmed | Axial-FNet: skin cancer image segmentation model based on fuzzy convolution combined with gated axial self-attention |
| title_short | Axial-FNet: skin cancer image segmentation model based on fuzzy convolution combined with gated axial self-attention |
| title_sort | axial fnet skin cancer image segmentation model based on fuzzy convolution combined with gated axial self attention |
| topic | image segmentation fuzzy learning gated axial self-attention gating mechanism convolutional neural network |
| url | http://www.cjist.com.cn/zh/article/doi/10.11959/j.issn.2096-6652.202520/ |
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