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<p>This study compares various preprocessing techniques for hyperspectral deep learning–based cancer diagnostics. The study considers different spectrum scaling and noise reduction options across spatial and spectral axes of hyperspectral datacubes, as well varying levels of blood and light reflections removal. We also examine how the size of the patches extracted from the hyperspectral data affects the models’ performance. We additionally explore various strategies to mitigate our dataset’s imbalance (where cancerous tissues are underrepresented). Our results indicate that. Scaling: Standardization significantly improves both sensitivity and specificity compared to Normalization. Larger input patch sizes enhance performance by capturing more spatial context. Noise reduction unexpectedly degrades performance. Blood filtering is more effective than filtering reflected light pixels, although neither approach produces significant results. By carefully maintaining consistent testing conditions, we ensure a fair comparison across preprocessing methods and reproducibility. Our findings highlight the necessity of careful preprocessing selection to maximize deep learning performance in medical imaging applications.</p>

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Exploring the role of preprocessing combinations in hyperspectral imaging for deep learning colorectal cancer detection

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月23日

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  • Mariia Tkachenko,
  • Benjamin Huber,
  • Serhii Hamotskyi,
  • Boris Jansen-Winkeln,
  • Ines Gockel,
  • Thomas Neumuth,
  • Hannes Köhler,
  • Marianne Maktabi

摘要

This study compares various preprocessing techniques for hyperspectral deep learning–based cancer diagnostics. The study considers different spectrum scaling and noise reduction options across spatial and spectral axes of hyperspectral datacubes, as well varying levels of blood and light reflections removal. We also examine how the size of the patches extracted from the hyperspectral data affects the models’ performance. We additionally explore various strategies to mitigate our dataset’s imbalance (where cancerous tissues are underrepresented). Our results indicate that. Scaling: Standardization significantly improves both sensitivity and specificity compared to Normalization. Larger input patch sizes enhance performance by capturing more spatial context. Noise reduction unexpectedly degrades performance. Blood filtering is more effective than filtering reflected light pixels, although neither approach produces significant results. By carefully maintaining consistent testing conditions, we ensure a fair comparison across preprocessing methods and reproducibility. Our findings highlight the necessity of careful preprocessing selection to maximize deep learning performance in medical imaging applications.