Abstract
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.
Abstract (translated)
宫颈癌仍然是一个重要的健康问题，特别是在发展中国家。早期检测对于有效的治疗至关重要。卷积神经网络（CNN）在自动化宫颈癌筛查中显示出前景，但其性能依赖于巴氏涂片图像的质量。本研究调查了各种图像预处理技术对使用SIPaKMeD数据集进行宫颈癌分类的CNN性能的影响。评估了三种预处理技术：Perona-Malik扩散（PMD）滤波器用于噪声减少、对比度受限自适应直方图均衡化（CLAHE）用于增强图像对比度以及提出的混合PMD滤波器-CLAHE方法。使用预训练模型，如ResNet-34、ResNet-50、SqueezeNet-1.0、MobileNet-V2、EfficientNet-B0、EfficientNet-B1、DenseNet-121和DenseNet-201对增强后的图像数据集进行了评估。结果显示，混合预处理PMD滤波器-CLAHE方法可以改善巴氏涂片图像质量和CNN架构性能，相较于原始图像，最大指标改进分别为：准确率提高13.62%，精确度提高10.04%，召回率提高13.08%以及F1分数提高14.34%。提出的混合PMD滤波器-CLAHE技术为利用CNN架构改善宫颈癌分类性能提供了新的视角。
URL
https://arxiv.org/abs/2506.15489
