Axial-FNet: skin cancer image segmentation model based on fuzzy convolution combined with gated axial self-attention

JIANG Shu; CHEN Kun; DING Weiping; ZHOU Tianyi; ZHU Yue

doi:10.11959/j.issn.2096-6652.202520

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Axial-FNet: skin cancer image segmentation model based on fuzzy convolution combined with gated axial self-attention

Papers and Reports | 更新时间：2025-07-21

- Axial-FNet: skin cancer image segmentation model based on fuzzy convolution combined with gated axial self-attention
- Chinese Journal of Intelligent Science and Technology Vol. 7, Issue 2, Pages: 221-233(2025)
- 作者机构：
  
  1.南通大学人工智能与计算机学院，江苏南通 226019
  2.南通大学张謇学院，江苏南通 226019
- 作者简介：
- 基金信息：
  
  The National Key R&D Plan of China(2024YFE0202700);The National Natural Science Foundation of China(62406153);The National College Students' Innovation and Entrepreneurship Training Program Project(202410304067Z);The Natural Science Foundation of Jiangsu Province(20231337);The General Program of the Natural Science Research of Higher Education of Jiangsu Province(23KJB520031;24KJB520032)
- DOI：10.11959/j.issn.2096-6652.202520
  CLC： TP391
- Received：12 February 2025，
  
  Revised：2025-04-21，
  
  Published：15 June 2025
- 稿件说明：
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姜舒,陈琨,丁卫平等.Axial-FNet：基于模糊卷积结合门控轴向自注意力的皮肤癌图像分割模型[J].智能科学与技术学报,2025,07(02):221-233.

JIANG Shu,CHEN Kun,DING Weiping,et al.Axial-FNet: skin cancer image segmentation model based on fuzzy convolution combined with gated axial self-attention[J].Chinese Journal of Intelligent Science and Technology,2025,07(02):221-233.
姜舒,陈琨,丁卫平等.Axial-FNet：基于模糊卷积结合门控轴向自注意力的皮肤癌图像分割模型[J].智能科学与技术学报,2025,07(02):221-233. DOI： 10.11959/j.issn.2096-6652.202520.

JIANG Shu,CHEN Kun,DING Weiping,et al.Axial-FNet: skin cancer image segmentation model based on fuzzy convolution combined with gated axial self-attention[J].Chinese Journal of Intelligent Science and Technology,2025,07(02):221-233. DOI： 10.11959/j.issn.2096-6652.202520.

摘要

皮肤癌图像分割是医学图像处理领域中的一项关键任务，现常用的分割算法在进行诊断时，无法很好地平衡局部细节信息和全局上下文信息的计算资源请求量。此外，肿瘤边界模糊且难以正确识别分割的问题也亟待解决。针对上述问题，提出了模糊卷积结合门控轴向自注意力的皮肤癌图像分割模型Axial-FNet，该模型由门控轴向自注意力分支和模糊卷积神经网络分支构成。在门控轴向自注意力分支的尾部，设置了门控权重控制器，以控制捕捉局部细节信息和全局上下文信息的比例和程度；融合模糊学习模块至卷积神经网络（CNN）中形成模糊神经网络分支，提取图像局部信息。该模型在降低计算量的同时提升了分割精确度。在ISIC 2017数据集和ISIC 2018数据集上的平均交并比（MIoU）、F1分数（F1-score）和准确率（Accuracy）分别达到了74.23%、83.05%和92.89%，80.91%、88.13%和93.10%。实验结果表明，Axial-FNet较其他多个先进分割模型而言，分割的精确度和可靠性更佳。

Abstract

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.

关键词

Keywords

references

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