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Patch-Based Convolutional Encoder: A Deep Learning Algorithm for Spectral Classification Balancing the Local and

Xin-Yu Lu1, Chen-Yue Wang2, Hui Tang1

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A new algorithm, patch-based convolutional encoder (PACE), enhances molecular vibrational spectroscopy. PACE improves spectral classification accuracy, especially for subtle differences, aiding in chemical analysis and early disease diagnosis.

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Area of Science:

  • Molecular spectroscopy
  • Chemometrics
  • Machine learning

Background:

  • Molecular vibrational spectroscopies (infrared absorption, Raman scattering) offer molecular fingerprint data for analysis.
  • Deep learning has improved spectral, spatial, and temporal resolutions in spectroscopy.
  • Current deep learning methods struggle with subtle spectral differences for accurate classification.

Purpose of the Study:

  • To develop a novel deep learning algorithm for enhanced spectral classification accuracy.
  • To effectively extract both local and global spectral features for improved analysis.
  • To address limitations in classifying subtle spectral variations.

Main Methods:

  • Developed a lightweight algorithm named patch-based convolutional encoder (PACE).
  • PACE segments spectra into patches to capture local information.
  • Depthwise separable convolutions are used to extract global information by correlating patches.

Main Results:

  • PACE achieved state-of-the-art performance across five open-source spectral datasets.
  • The algorithm demonstrated superior performance on more challenging classification tasks.
  • Achieved 92.1% accuracy in Raman identification of pathogen-derived extracellular vesicles, outperforming ResNet (85.1%) and ViT (86.0%).

Conclusions:

  • PACE effectively balances local and global spectral information for improved classification.
  • The algorithm's ability to recognize subtle differences facilitates vibrational spectroscopy applications.
  • Potential applications include revealing chemical reaction mechanisms and enabling early diagnosis in life sciences.