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English(EN) Contrast Enhancement or Noise Reduction? On Improving Cervical Cancer Classification

对比度增强提升了CNN宫颈癌分类的准确性

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新发表在arXiv上的研究论文探讨了使用深度学习模型改进宫颈癌分类的图像预处理技术的有效性。该研究评估了两种方法：用于降噪的PMD滤波器和用于对比度增强的CLAHE，应用于三种CNN架构：ResNet-34、MobileNet-V2和DenseNet-121。结果表明，CLAHE显著提高了ResNet-34和DenseNet-121的分类准确性，而PMD滤波器仅显示出有限的改进，并略微降低了MobileNet-V2的性能，这表明对于此任务，对比度增强比降噪更有益。 AI

影响 证明了对比度增强技术可以显著提高深度学习模型在医学图像分类任务中的准确性。

排序理由 发表在arXiv上的研究论文，详细介绍了医学图像分类图像预处理技术的系统比较。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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对比度增强提升了CNN宫颈癌分类的准确性

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发表在arXiv上的研究论文，详细介绍了医学图像分类图像预处理技术的系统比较。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Ach Khozaimi, Ulfatun Nahdhiyah ·

    对比度增强还是降噪？论改进宫颈癌分类

    arXiv:2610.11086v1 Announce Type: new Abstract: 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 performan…

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