对比度增强还是降噪？关于改进宫颈癌分类的研究
Contrast Enhancement or Noise Reduction? On Improving Cervical Cancer Classification
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中文总结 AI 辅助
本研究评估了PMD滤波器和CLAHE两种预处理算法对ResNet-34等三种CNN的巴氏涂片图像分类性能的影响，发现CLAHE提升效果更显著，为宫颈癌分类提供了新流程。
中文摘要 AI 辅助
目的：宫颈癌是全球主要的致死原因之一，深度学习在医学图像分类中已展现出良好性能，但图像预处理算法对分类性能的影响在文献中研究不足。本研究旨在评估图像预处理算法对用于巴氏涂片图像分类的卷积神经网络（CNN）性能的影响。方法：使用SIPaKMeD数据集对三种CNN架构（ResNet-34、MobileNet-V2和DenseNet-121）进行训练和评估，应用两种预处理算法：用于降噪的PMD滤波器和用于对比度增强的CLAHE，采用混淆矩阵评估模型性能。结果：预处理提升了所有模型的分类性能，CLAHE使ResNet-34的准确率从76.73%显著提升至84.16%，DenseNet-121的准确率也从76.73%提升至84.16%；PMD滤波器的提升效果有限，且略微降低了MobileNet-V2的性能。创新性：本研究系统比较了不同CNN架构下对比度增强与降噪技术的效果，证明对比度增强在提升CNN性能方面比降噪更有效，为改进宫颈癌分类提供了新的流程。
英文摘要
Purpose: Cervical cancer is one of the leading causes of mortality worldwide. Deep learning has shown promising performance in medical image classification. The influence of image preprocessing algorithms on classification performance remains insufficiently investigated in the literature. This research aims to evaluate the impact of image preprocessing algorithms on the performance of CNNs for Pap smear image classification. Methods: Three CNN architectures (ResNet-34, MobileNet-V2, and DenseNet-121) were trained and evaluated using the SIPaKMeD dataset. Two preprocessing algorithms were applied: the PMD filter for noise reduction and CLAHE for contrast enhancement. The model performance was assessed using a confusion matrix. Results: Preprocessing improved the classification performance of all models. CLAHE significantly increased the accuracy of ResNet-34 from 76.73% to 84.16% and DenseNet-121 from 76.73% to 84.16%. The PMD filter yielded limited improvement and slightly reduced the MobileNet-V2 performance. Novelty: This research provides a systematic comparison of contrast enhancement and noise reduction techniques across CNN architectures. This research demonstrates that contrast enhancement is more effective than noise reduction in improving CNN performance. The research provides new pipelines for improving cervical cancer classification.
发表机构
- University of Trunodjoyo Madura（特鲁诺乔莫大学马都拉分校）
- District General Hospital (RSUD Syarifah Ambami Rato Ebu)（地区综合医院（RSUD Syarifah Ambami Rato Ebu））
机构由 AI 辅助整理，请以论文原文为准。
