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Decoding Optical Data with Machine Learning.

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Machine learning (ML) rapidly analyzes optical data for disease diagnosis and materials science. This review explores ML algorithms for optical data decoding, highlighting future opportunities.

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Area of Science:

  • Optics and Data Science
  • Interdisciplinary applications of optical techniques

Background:

  • Optical spectroscopy and imaging are vital across diverse fields, including medicine, biology, IT, and materials science.
  • Machine learning (ML) has emerged as a powerful tool for analyzing complex optical data, offering speed and accuracy.

Purpose of the Study:

  • To review machine learning algorithms applied to optical data analysis.
  • To demonstrate the effectiveness of ML in decoding optical spectra and images.
  • To explore the broad applications of ML in optical science and related fields.

Main Methods:

  • Comprehensive literature review of machine learning algorithms used in optical data analysis.
  • Categorization of ML techniques based on their application in optical spectroscopy and imaging.
  • Analysis of case studies showcasing ML-driven optical data interpretation.

Main Results:

  • Machine learning significantly enhances the speed and accuracy of optical data analysis.
  • Various ML algorithms show promise in interpreting complex optical spectra and images.
  • Successful applications demonstrated in disease diagnosis, biological studies, and materials science.

Conclusions:

  • Machine learning provides a valid and powerful approach for decoding optical data.
  • Emerging opportunities exist at the intersection of optics, data science, and ML.
  • Further research is needed to address remaining challenges and unlock full potential.