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Code-Free Machine Learning Solutions for Microscopy Image Processing: Deep Learning.

Elizaveta Chechekhina¹, Nikita Voloshin¹, Konstantin Kulebyakin¹

¹Department of Biochemistry and Regenerative Biomedicine, Faculty of Medicine, Lomonosov Moscow State University, Moscow, Russia.

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Summary

This review simplifies deep learning for microscopy image analysis, offering biologists accessible, no-code tools. It focuses on practical applications rather than complex programming, empowering researchers without coding skills.

Keywords:

artificial intelligence deep learning open-source code-free microscopy image analysis

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Rapid Analysis and Exploration of Fluorescence Microscopy Images

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Area of Science:

Computational Biology
Image Analysis
Machine Learning Applications

Background:

Machine learning, particularly deep learning, has revolutionized microscopy image processing.
Traditional methods often require advanced programming and mathematical expertise, limiting accessibility for biologists.
There is a growing need for user-friendly tools for analyzing complex microscopy data.

Purpose of the Study:

To provide a comprehensive review of widely used deep learning algorithms for microscopy image processing.
To emphasize algorithms suitable for biologists with limited or no programming expertise.
To explain the capabilities and applications of these algorithms without delving into complex mathematics or coding.

Main Methods:

Exploration of popular deep learning approaches for microscopy image analysis.
Focus on algorithms available on open platforms with no-code requirements.
Detailed descriptions and links to accessible tools are provided.

Main Results:

Identification of deep learning algorithms applicable to biological microscopy image processing.
Demonstration that these algorithms can be used effectively without programming knowledge.
Highlighting the adaptability of these methods for various computer vision tasks beyond biology.

Conclusions:

Deep learning tools can significantly aid biologists in microscopy image analysis without requiring coding skills.
The review offers a gateway for biologists to leverage advanced AI for their research.
The principles discussed are transferable to other scientific fields and computer vision applications.