用AI自动分类宫颈癌细胞，深度学习模型准确率达93.06%。
Comparative Analysis of Machine Learning and Deep Learning Models for Classifying Squamous Epithelial Cells of the Cervix
- 对比多种机器学习与深度学习模型进行细胞分类
- ResNet-50准确率最高，达93.06%
- 适合医学影像分析与宫颈癌早筛研究者
宫颈是连接子宫与阴道的狭窄部位，其鳞状上皮异常增生可导致宫颈癌。巴氏涂片通过采集宫颈表面细胞并在显微镜下观察，用于检测宫颈癌。对于大规模筛查，醋酸视觉检查成本低且敏感度高，而巴氏涂片因特异度更高也适用于大规模筛查。然而，当前巴氏涂片分析依赖人工，耗时费力且易出错。因此，亟需基于人工智能的自动化细胞分类方法。本研究旨在将巴氏涂片图像中的细胞分为五类：表层-中间型、基底型、挖空细胞、角化不良细胞和化生细胞。采用多种机器学习（梯度提升、随机森林、支持向量机、k近邻）及深度学习（如ResNet-50）模型进行分类。结果显示，机器学习模型表现良好，但ResNet-50性能最优，分类准确率达93.06%。该研究证明深度学习在细胞级分类中的高效性，具有辅助宫颈癌早期诊断的潜力。
原文摘要 · Abstract (English)
The cervix is the narrow end of the uterus that connects to the vagina in the female reproductive system. Abnormal cell growth in the squamous epithelial lining of the cervix leads to cervical cancer in females. A Pap smear is a diagnostic procedure used to detect cervical cancer by gently collecting cells from the surface of the cervix with a small brush and analyzing their changes under a microscope. For population-based cervical cancer screening, visual inspection with acetic acid is a cost-effective method with high sensitivity. However, Pap smears are also suitable for mass screening due to their higher specificity. The current Pap smear analysis method is manual, time-consuming, labor-intensive, and prone to human error. Therefore, an artificial intelligence (AI)-based approach for automatic cell classification is needed. In this study, we aimed to classify cells in Pap smear images into five categories: superficial-intermediate, parabasal, koilocytes, dyskeratotic, and metaplastic. Various machine learning (ML) algorithms, including Gradient Boosting, Random Forest, Support Vector Machine, and k-Nearest Neighbor, as well as deep learning (DL) approaches like ResNet-50, were employed for this classification task. The ML models demonstrated high classification accuracy; however, ResNet-50 outperformed the others, achieving a classification accuracy of 93.06%. This study highlights the efficiency of DL models for cell-level classification and their potential to aid in the early diagnosis of cervical cancer from Pap smear images.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。