智能科学与技术学报2023，Vol.5Issue(4)：505-514,10.DOI:10.11959/j.issn.2096-6652.202343
基于改进EfficientNet的乳腺肿瘤诊断
Diagnostic of breast tumors based on improved EfficientNet
摘要
Abstract
Breast tumors adversely affect the holistic well-being of women.Histopathological images are a critical sub-stantiation 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 Efficient-Net was employed for the diagnosis of breast tumors,which enabled the network model to learn the features of the dis-ease automatically and improve the accuracy of the diagnosis of breast tumor types.Firstly,the convolutional block atten-tion 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 ap-plied to improve the convergence speed of the model.Finally,Experiments showed that the improved EfficientNet net-work 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
乳腺肿瘤/EfficientNet/图像分类/卷积神经网络Key words
breast tumor/EfficientNet/image classification/convolutional neural network分类
信息技术与安全科学引用本文复制引用
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):505-514,10.基金项目
国家自然科学基金项目(No.61473048)The National Natural Science Foundation of China(No.61473048) （No.61473048）
