文章：

SPP-SegNet与SE-DenseNet201：一种用于宫颈细胞分割与分类的双模型方法

SPP-SegNet and SE-DenseNet201: A Dual-Model Approach for Cervical Cell Segmentation and Classification

原文发布日期：27 June 2025

DOI: 10.3390/cancers17132177

类型: Article

开放获取: 是

英文摘要：

Background/Objectives: Cervical cancer, the fourth most common malignancy in women worldwide, continues to pose a significant threat to global health. Manual examination of the Pap smear image is time-consuming, labor-intensive, and prone to human error due to the large number of slides and subjective judgment. This study proposes a novel SegNet-based spatial pyramid pooling (SPP-SegNet) deep learning model for segmentation and a Squeeze-and-Excitation-based (SE-DenseNet201) model for classification, aimed at improving the accuracy of cervical cancer detection. Methods: The model incorporates the SPP bottleneck and atrous convolution in the SegNet framework, allowing for the extraction of multiscale spatial features and improving segmentation performance. The segmentation output is used as input for the classification task. The proposed method is evaluated on the Pomeranian and SIPaKMeD datasets. Results: Segmentation results show that SPP-SegNet achieves 98.53% accuracy on the Pomeranian data set, exceeding standard SegNet, 97.86%. It also achieves 94.15% accuracy on the SIPaKMeD dataset, outperforming the standard SegNet, which is 90.95%. For classification, SE-DenseNet201 achieves 93% and 99% accuracy for the Pomeranian and SIPaKMeD binary classification, respectively, using the bounding box input. Conclusions: These results show that SPP-SegNet and SE-DenseNet201 can potentially automate cervical cell segmentation and classification, facilitating the early detection and diagnosis of cervical cancer.

摘要翻译： 

背景/目的：宫颈癌作为全球女性第四大常见恶性肿瘤，持续对全球健康构成重大威胁。由于涂片数量庞大且依赖主观判断，传统巴氏涂片图像人工检测方法耗时费力且易出现人为误差。本研究提出一种基于SegNet的新型空间金字塔池化（SPP-SegNet）深度学习模型用于细胞分割，并结合基于压缩激励机制的SE-DenseNet201模型进行分类，旨在提升宫颈癌检测的准确性。方法：该模型在SegNet框架中融入SPP瓶颈结构与空洞卷积，能够提取多尺度空间特征并提升分割性能。分割输出结果将作为分类任务的输入数据。所提方法在波美拉尼亚数据集和SIPaKMeD数据集上进行了验证。结果：分割实验显示，SPP-SegNet在波美拉尼亚数据集上达到98.53%的准确率，优于标准SegNet的97.86%；在SIPaKMeD数据集上取得94.15%的准确率，超越标准SegNet的90.95%。分类任务中，采用边界框输入的SE-DenseNet201模型在波美拉尼亚和SIPaKMeD数据集的二分类任务中分别达到93%和99%的准确率。结论：实验结果表明，SPP-SegNet与SE-DenseNet201模型有望实现宫颈细胞分割与分类的自动化，为宫颈癌的早期检测与诊断提供技术支持。

原文链接：

SPP-SegNet and SE-DenseNet201: A Dual-Model Approach for Cervical Cell Segmentation and Classification