用混合滤波增强宫颈癌涂片图像，提升深度学习分类效果
Advanced cervical cancer classification: enhancing pap smear images with hybrid PMD Filter-CLAHE
- 结合PMD去噪与CLAHE增强，提出混合图像预处理方法
- 在SIPaKMeD数据集上使分类准确率提升13.62%、F1-score提升14.34%
- 适合医学图像分析与宫颈癌筛查自动化研究者参考
宫颈癌仍是发展中国家的重大健康问题，早期检测对治疗至关重要。卷积神经网络（CNN）在自动宫颈癌筛查中表现良好，但其性能依赖于巴氏涂片图像质量。本研究评估了多种图像预处理技术对基于SIPaKMeD数据集的CNN分类性能的影响，包括Perona-Malik扩散（PMD）滤波去噪、对比度受限自适应直方图均衡化（CLAHE）增强对比度，以及提出的混合PMD-CLAHE方法。增强后的图像数据在ResNet-34、ResNet-50、SqueezeNet-1.0、MobileNet-V2、EfficientNet-B0、EfficientNet-B1、DenseNet-121和DenseNet-201等预训练模型上进行测试。结果表明，混合预处理方法显著提升图像质量与CNN性能，最大提升达：准确率+13.62%，精确率+10.04%，召回率+13.08%，F1-score+14.34%。
原文摘要 · Abstract (English)
Cervical cancer remains a significant health problem, especially in developing countries. Early detection is critical for effective treatment. Convolutional neural networks (CNN) have shown promise in automated cervical cancer screening, but their performance depends on Pap smear image quality. This study investigates the impact of various image preprocessing techniques on CNN performance for cervical cancer classification using the SIPaKMeD dataset. Three preprocessing techniques were evaluated: perona-malik diffusion (PMD) filter for noise reduction, contrast-limited adaptive histogram equalization (CLAHE) for image contrast enhancement, and the proposed hybrid PMD filter-CLAHE approach. The enhanced image datasets were evaluated on pretrained models, such as ResNet-34, ResNet-50, SqueezeNet-1.0, MobileNet-V2, EfficientNet-B0, EfficientNet-B1, DenseNet-121, and DenseNet-201. The results show that hybrid preprocessing PMD filter-CLAHE can improve the Pap smear image quality and CNN architecture performance compared to the original images. The maximum metric improvements are 13.62% for accuracy, 10.04% for precision, 13.08% for recall, and 14.34% for F1-score. The proposed hybrid PMD filter-CLAHE technique offers a new perspective in improving cervical cancer classification performance using CNN architectures.
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