生物医学工程研究2026，Vol.45Issue(3)：197-203,7.DOI:10.19529/j.cnki.1672-6278.2026.03.03
融合注意力机制与迁移学习的宫颈细胞图像多分类优化研究
Optimization research on multi-classification of cervical cell images integrating attention mechanism and transfer learning
杨晨 1胡晓鹏2
作者信息
- 1. 西南交通大学 计算机与人工智能学院,成都 611756||万锦医工(沈阳)科技有限公司,沈阳 110000
- 2. 西南交通大学 计算机与人工智能学院,成都 611756
- 折叠
摘要
Abstract
Aiming at the problems of high similarity of morphological features of similar categories of cervical cell images and insuf-ficient generalization ability of the model caused by the limited size of the dataset,we proposed an improved Resnet34 model for the multi-classification task of cervical cell images.Firstly,an attention module was introduced into the residual blocks of Resnet34,and the activation function ReLU was replaced with PReLU,thereby enhancing the model's ability to capture details and its adaptability.Secondly,the transfer learning strategy was adopted to enhance the generalization performance of the model.The result showed that the accuracy of the improved model reached 96.78%,was 1.32%higher than that of the original Resnet34.The research can effectively capture the subtle differences in the image morphology of cervical cells belonging to similar categories,optimize the recognition perform-ance and generalization ability of the model,can provide reliable technical support for the early automated screening of cervical cancer.关键词
多分类/宫颈细胞图像/Resnet34/注意力机制/迁移学习Key words
Multi-classification/Cervical cell images/Resnet34/Attention mechanism/Transfer learning分类
医药卫生引用本文复制引用
杨晨,胡晓鹏..融合注意力机制与迁移学习的宫颈细胞图像多分类优化研究[J].生物医学工程研究,2026,45(3):197-203,7.
