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<p>Accurate denoising of Pap smear images is essential for reliable cytopathological analysis and diagnosis. This study presents PAP-Net, a systematic framework designed to enhance the quality of Pap smear images by integrating multiple state-of-the-art denoising techniques, including DnCNN, FFDNet, Noise2Noise, SwinIR, BM3D, and Non-Local Means. The framework systematically evaluates these models on a dataset of 5123 clinical Pap smear images to determine the most effective approach for noise reduction while preserving the cytoplasm and nucleus of Pap smear images. Experimental results demonstrate that SwinIR consistently outperforms other models across various qualitative and quantitative metrics. Specifically, SwinIR achieves an SSIM of 0.91, SNR of 21.2, PSNR of 29.5, and MSE of 0.001122, excelling in maintaining morphological details essential for accurate cytological assessment. By significantly reducing noise while retaining key diagnostic features, PAP-Net provides a structured approach to optimize image quality, thereby facilitating more precise and efficient cervical cancer diagnoses. These findings highlight the potential of PAP-Net to advance cytopathological analysis and improve patient diagnosis in clinical settings.</p>

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PAP-Net: A Systematic Framework for Denoising Pap Smear Images

  • 期刊论文

  • 发表日期：

    2025年5月14日

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  • Nahida Nazir,
  • Abid Sarwar,
  • Baljit Singh Saini

摘要

Accurate denoising of Pap smear images is essential for reliable cytopathological analysis and diagnosis. This study presents PAP-Net, a systematic framework designed to enhance the quality of Pap smear images by integrating multiple state-of-the-art denoising techniques, including DnCNN, FFDNet, Noise2Noise, SwinIR, BM3D, and Non-Local Means. The framework systematically evaluates these models on a dataset of 5123 clinical Pap smear images to determine the most effective approach for noise reduction while preserving the cytoplasm and nucleus of Pap smear images. Experimental results demonstrate that SwinIR consistently outperforms other models across various qualitative and quantitative metrics. Specifically, SwinIR achieves an SSIM of 0.91, SNR of 21.2, PSNR of 29.5, and MSE of 0.001122, excelling in maintaining morphological details essential for accurate cytological assessment. By significantly reducing noise while retaining key diagnostic features, PAP-Net provides a structured approach to optimize image quality, thereby facilitating more precise and efficient cervical cancer diagnoses. These findings highlight the potential of PAP-Net to advance cytopathological analysis and improve patient diagnosis in clinical settings.