基于改进EfficientNet的乳腺肿瘤诊断

方祯祺; 李雪; 莫红

doi:10.11959/j.issn.2096-6652.202343

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基于改进EfficientNet的乳腺肿瘤诊断

学术论文 | 更新时间：2024-04-07

- 基于改进EfficientNet的乳腺肿瘤诊断
- Diagnostic of breast tumors based on improved EfficientNet
- 智能科学与技术学报 2023年5卷第4期页码：505-514
- 作者机构：
  
  长沙理工大学电气与信息工程学院，湖南长沙 410114
- 作者简介：
  
  [ "方祯祺（1999- ），女，长沙理工大学电气与信息工程学院硕士生，主要研究方向为智慧医疗。" ]
  [ "李雪（1998- ），女，长沙理工大学电气与信息工程学院硕士生，主要研究方向为智慧医疗。" ]
  [ "莫红（1972- ），女，长沙理工大学电气与信息工程学院教授，主要研究方向为主要研究方向为智慧医疗、模糊AI和复杂系统管理与控制。" ]
- 基金信息：
  
  国家自然科学基金项目(61473048)
- DOI：10.11959/j.issn.2096-6652.202343
  中图分类号： TP391.4
- 收稿：2023-08-06，
  
  修回：2023-11-13，
  
  纸质出版：2023-12-15
- 稿件说明：
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方祯祺, 李雪, 莫红. 基于改进EfficientNet的乳腺肿瘤诊断[J]. 智能科学与技术学报, 2023,5(4):505-514.

FANG Zhenqi, LI Xue, MO Hong. Diagnostic of breast tumors based on improved EfficientNet[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(4): 505-514.
方祯祺, 李雪, 莫红. 基于改进EfficientNet的乳腺肿瘤诊断[J]. 智能科学与技术学报, 2023,5(4):505-514. DOI： 10.11959/j.issn.2096-6652.202343.

FANG Zhenqi, LI Xue, MO Hong. Diagnostic of breast tumors based on improved EfficientNet[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(4): 505-514. DOI： 10.11959/j.issn.2096-6652.202343.

摘要

乳腺肿瘤严重影响女性的身心健康。病理学图像分析是医生诊断乳腺肿瘤的一个重要方法，不同类型肿瘤细胞的结构具有高度的相关性，这使得常规方法的诊断不易进行。改进的EfficientNet被用来诊断乳腺肿瘤，其使网络模型能自动学习疾病的特征并提高乳腺肿瘤诊断的准确率。基于此，首先，采用卷积块注意力模型提取乳腺肿瘤病理图像的有效特征；其次，引入分组卷积和通道混洗操作来提高模型的特征表达能力；再次，利用Hard-Swish激活函数提升模型的收敛速度；最后，实验验证了改进后的EfficientNet在BreakHis数据集上的8分类准确率达到98.4%，该方法成为乳腺肿瘤诊断的一个有力的决策辅助工具。

Abstract

Breast tumors adversely affect the holistic well-being of women. Histopathological images are a critical substantiation for doctors to diagnose breast tumor types. The structure of various types of tumor cells exhibits significant correlations

thereby posing challenges to the diagnosis using conventional methods. In this work

the enhanced EfficientNet was employed for the diagnosis of breast tumors

which enabled the network model to learn the features of the disease automatically and improve the accuracy of the diagnosis of breast tumor types. Firstly

the convolutional block attention module was used to extract effective features. Secondly

the group convolution and channel shuffle operations were introduced to improve the feature representation ability of the model. Thirdly

the Hard-Swish activation function was applied to improve the convergence speed of the model. Finally

Experiments showed that the improved EfficientNet network achieved 98.4% accuracy in eight classifications on the BreakHis dataset

which was expected to act a decision aid tool in breast tumor diagnostic research.

关键词

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references

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