用深度学习分割并分类宫颈涂片图像，辅助早期癌变检测
Segmentation and Classification of Pap Smear Images for Cervical Cancer Detection Using Deep Learning
- 先用U-Net分割细胞，再分类，提升诊断准确性
- 分割后模型精确率提高0.41%，F1值提升1.30%
- 适合需要辅助诊断的临床医生和医学影像研究者
宫颈癌仍是全球女性癌症死亡的主要原因，早期通过巴氏涂片检查可显著降低死亡率，但人工阅片耗时且易出错。本研究提出一种融合U-Net分割与分类模型的深度学习框架，使用公开的Herlev Pap Smear Dataset进行训练与评估。通过对比基于分割图像与非分割图像的模型性能，发现使用分割图像使精确率提升约0.41%，F1-score提升约1.30%，表明分类性能略有改善。尽管分割有助于特征提取，但其对分类性能的提升有限。该框架可作为临床辅助工具，帮助病理医生实现更早诊断。
原文摘要 · Abstract (English)
Cervical cancer remains a significant global health concern and a leading cause of cancer-related deaths among women. Early detection through Pap smear tests is essential to reduce mortality rates; however, the manual examination is time consuming and prone to human error. This study proposes a deep learning framework that integrates U-Net for segmentation and a classification model to enhance diagnostic performance. The Herlev Pap Smear Dataset, a publicly available cervical cell dataset, was utilized for training and evaluation. The impact of segmentation on classification performance was evaluated by comparing the model trained on segmented images and another trained on non-segmented images. Experimental results showed that the use of segmented images marginally improved the model performance on precision (about 0.41 percent higher) and F1-score (about 1.30 percent higher), which suggests a slightly more balanced classification performance. While segmentation helps in feature extraction, the results showed that its impact on classification performance appears to be limited. The proposed framework offers a supplemental tool for clinical applications, which may aid pathologists in early diagnosis.
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。