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An Integrated Raman Platform with Embedded AI for Intraoperative Real-Time Cancer Detection.

Dinkar Regmi1, Ya Zhang1, Huaizhi Wang1

  • 1Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, Louisiana 70803, United States.

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Summary
This summary is machine-generated.

We developed an integrated Raman spectroscopy platform with a Python GUI, unifying instrument control, data processing, and AI analysis for reproducible, auditable results in sample classification.

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Area of Science:

  • Spectroscopy
  • Data Science
  • Biotechnology

Background:

  • Raman spectroscopy workflows are often fragmented, hindering reproducibility and decision-making.
  • Proprietary tools and scripts create inefficiencies in data processing and analysis.

Purpose of the Study:

  • To present an integrated Raman platform with a Python-based GUI.
  • To unify instrument control, automate data acquisition and processing, and incorporate AI for real-time sample classification.
  • To ensure auditability through automatic archiving of all outputs.

Main Methods:

  • Developed a Python GUI application integrated with optical systems.
  • Automated data acquisition, noise removal, and signal processing.
  • Embedded a 1D convolutional neural network (CNN) for Raman spectra classification.
  • Validated the platform with Tylenol and human laryngeal tissue samples.

Main Results:

  • The platform demonstrated consistent results compared to other studies and commercial software.
  • The embedded CNN achieved high accuracy (0.8929 ± 0.0213) for classifying biological Raman spectra.
  • Real-time classification had an average latency of 2.82 seconds.

Conclusions:

  • The integrated platform offers an end-to-end, auditable workflow for Raman spectroscopy.
  • It unifies device control, acquisition, processing, visualization, and AI inference.
  • This approach enhances reproducibility, auditability, and efficiency in scientific research.