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Updated: Feb 17, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Traditional machine learning in biomedical image analysis: before you go too deep.

Elizaveta Chechekhina1, Nikita Voloshin1, Maksim Solopov2

  • 1Medical Research and Educational Institute, Lomonosov Moscow State University, Moscow, Russia.

Frontiers in Artificial Intelligence
|February 16, 2026
PubMed
Summary

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Traditional machine learning (TML) remains crucial for biomedical image analysis, excelling in multimodal data integration and interpretability, especially with limited data. This review highlights TML

Area of Science:

  • Biomedical image analysis
  • Machine learning
  • Medical imaging

Background:

  • Traditional machine learning (TML) algorithms offer advantages in multimodal data integration, interpretability, computational efficiency, and robustness on smaller datasets.
  • Deep learning (DL) currently dominates medical image analysis due to superior performance and end-to-end feature learning.
  • Despite DL's prevalence, TML retains unique value in specific biomedical imaging applications.

Purpose of the Study:

  • To provide a comprehensive review of TML applications across various biomedical imaging modalities.
  • To highlight the core principles, practical implementation, and benefits of TML in the context of deep learning.
  • To analyze the continued relevance and unique value proposition of TML in medical image analysis.

Main Methods:

Keywords:
biomedical image analysisobject classificationradiomicssemantic segmentationtraditional machine learning

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  • Review of fundamental machine learning concepts relevant to biomedical imaging.
  • Description of key biomedical imaging tasks addressed by TML.
  • Analysis of prominent examples showcasing TML's strengths.
  • Identification of popular platforms enabling TML utilization by clinicians and researchers.

Main Results:

  • TML is effective for multimodal data processing, applications with limited data, and scenarios requiring high interpretability.
  • TML offers advantages in computational efficiency and robustness, particularly on smaller datasets.
  • Democratized tools and validated clinical studies support the continued use of TML.

Conclusions:

  • Traditional machine learning remains a vital methodology for extracting quantitative and qualitative insights from biomedical image data.
  • TML offers unique value in specific niches of medical image analysis, complementing deep learning approaches.
  • The continued relevance of TML in both research and clinical practice is ensured by its inherent strengths and increasing accessibility.