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<p>The fact that lung cancer continues to be the largest cause of cancer-related death globally highlights the importance of early detection and precise diagnosis. Low Dose Computed Tomography (CT) scanning has emerged as a potential screening method for lung cancer detection in its early stages. However, the inherent noise and artifacts present in low-dose CT images pose challenges for accurate analysis and interpretation. This work contains a comparative analysis of various pre-processing techniques aimed at enhancing the diagnostic potential of LDCT scan images for the detection of lung cancer in its early stages. Numerous pre-processing methods, including the Gaussian filter, the bilateral approach, the wavelet method, etc., were used in this work. Out of these techniques, the BM3D technique performs best. The results demonstrate the effectiveness of pre-processing techniques in improving the diagnostic accuracy of LDCT scan images for lung cancer detection.</p>

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Comparative analysis of pre-processing techniques with low dose CT scan images for early detection of lung cancer

  • 期刊论文

  • 发表日期：

    2025年9月16日

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  • Gagan Thakral,
  • Sapna Gambhir,
  • Umesh Kumar

摘要

The fact that lung cancer continues to be the largest cause of cancer-related death globally highlights the importance of early detection and precise diagnosis. Low Dose Computed Tomography (CT) scanning has emerged as a potential screening method for lung cancer detection in its early stages. However, the inherent noise and artifacts present in low-dose CT images pose challenges for accurate analysis and interpretation. This work contains a comparative analysis of various pre-processing techniques aimed at enhancing the diagnostic potential of LDCT scan images for the detection of lung cancer in its early stages. Numerous pre-processing methods, including the Gaussian filter, the bilateral approach, the wavelet method, etc., were used in this work. Out of these techniques, the BM3D technique performs best. The results demonstrate the effectiveness of pre-processing techniques in improving the diagnostic accuracy of LDCT scan images for lung cancer detection.