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<p>Raman Spectroscopy (RS) is a potent analytical approach used to probe the vibrational states of unique spectral fingerprints of samples. These spectral fingerprints have various applications, including analysis of non-destructive chemical composition and predictive modeling, from material science to biomedical diagnostics. The analysis of Raman spectra demands highly sophisticated techniques due to its label-free nature, necessitating adopting machine learning (ML) and deep learning (DL) methods. Within this realm, DL and hybrid deep learning (HDL) have emerged as promising analytical tools, demonstrating substantial strides in the analysis of Raman spectral data. This review explores recent advancements in ML especially DL and HDL techniques for liquid biopsy analysis using RS, presenting a novel and non-invasive approach to cancer detection and monitoring, highlighting their potential to serve as a digital pathology analytical tool for the detection of cancer while addressing the existing challenges in their application.</p>

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Exploring the Potential of Raman Spectroscopy in Digital Pathology: A Comprehensive Review with Respect to Innovative Hybrid Deep Learning Approaches

  • 期刊论文

  • 发表日期：

    2025年4月15日

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  • V. S. Renjith,
  • Subha Hency Jose,
  • S. Thomas George,
  • P. Manimegalai

摘要

Raman Spectroscopy (RS) is a potent analytical approach used to probe the vibrational states of unique spectral fingerprints of samples. These spectral fingerprints have various applications, including analysis of non-destructive chemical composition and predictive modeling, from material science to biomedical diagnostics. The analysis of Raman spectra demands highly sophisticated techniques due to its label-free nature, necessitating adopting machine learning (ML) and deep learning (DL) methods. Within this realm, DL and hybrid deep learning (HDL) have emerged as promising analytical tools, demonstrating substantial strides in the analysis of Raman spectral data. This review explores recent advancements in ML especially DL and HDL techniques for liquid biopsy analysis using RS, presenting a novel and non-invasive approach to cancer detection and monitoring, highlighting their potential to serve as a digital pathology analytical tool for the detection of cancer while addressing the existing challenges in their application.