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Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition:

Priya Rani1, Shallu Kotwal2, Jatinder Manhas3

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Machine learning (ML) and deep learning automate microorganism recognition from images, a crucial but tedious task in microbiology. This review analyzes 100 studies from 1995-2021 on ML techniques for microbial image analysis.

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Area of Science:

  • Microbiology
  • Computer Science
  • Bioinformatics

Background:

  • Microorganisms are vital for Earth's biodiversity and ecosystems.
  • Accurate microbial identification is essential for microbiological research and experimentation.
  • Traditional methods for microorganism recognition are time-consuming and labor-intensive.

Purpose of the Study:

  • To systematically review machine learning (ML) and deep learning (DL) applications in microorganism image recognition.
  • To analyze trends in image pre-processing, feature extraction, and classification techniques.
  • To identify methodological limitations and technical advancements in the field from 1995 to 2021.

Main Methods:

  • Systematic literature review of 100 research publications.
  • Chronological analysis of studies published between 1995 and 2021.
  • Investigation of research questions covering image processing, feature extraction, classification, and evaluation metrics.

Main Results:

  • Identified key ML and DL techniques employed for microbial image analysis.
  • Documented the evolution of methodologies and technical developments over time.
  • Highlighted common challenges and limitations in current research.

Conclusions:

  • ML and DL techniques offer significant potential for automating and improving microorganism image recognition.
  • This review provides a comprehensive overview of the field, aiding researchers in selecting appropriate methodologies.
  • Further research is needed to address existing challenges and enhance the effectiveness of ML/DL in microbiology.