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Deep learning method for malaria parasite evaluation from microscopic blood smear.

Abhinav Dahiya1, Devvrat Raghuvanshi1, Chhaya Sharma1

  • 1Department of Computer Science and Engineering, University Institute of Engineering and Technology, Maharshi Dayanand University, Rohtak, Haryana, India.

Artificial Intelligence in Medicine
|March 19, 2025
PubMed
Summary

Automated malaria diagnostics show promise for improved accuracy. Deep learning models achieve high performance, but standardization and real-world application challenges persist for global malaria control.

Keywords:
Artificial intelligenceDeep learningMachine learningMalariaPRISMAPlasmodium sppThin & thick blood smearVOS Viewer

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Area of Science:

  • Medical diagnostics
  • Parasitology
  • Computer science

Background:

  • Malaria causes significant global morbidity and mortality, with approximately 597,000 deaths reported in 2024.
  • Automated analysis of Plasmodium parasites in blood smears offers potential to enhance diagnostic accuracy and efficiency.

Purpose of the Study:

  • To systematically review current methodologies for automated malaria diagnostics.
  • To examine computer-assisted methods, databases, staining techniques, and diagnostic models.
  • To identify limitations and contributions of recent studies in automated malaria detection.

Main Methods:

  • Systematic literature review adhering to PRISMA guidelines.
  • Searched Web of Science and Scopus for peer-reviewed studies from 2020-2024.
  • Included studies using deep learning and machine learning for automated malaria detection from blood smears.

Main Results:

  • The NIH database is a standardized resource for malaria diagnostics.
  • Giemsa-stained thin blood smears are optimal for Plasmodium lifecycle observation.
  • Identified top-performing models: ResNet/VGG (99.12% accuracy), popular custom CNNs (58% of studies), and CADx models.

Conclusions:

  • Automated malaria diagnostics hold significant potential for reducing errors and improving accuracy.
  • Deep learning models show high performance, yet data standardization and real-world implementation require further attention.
  • Addressing current challenges can lead to more reliable and scalable diagnostic tools for global malaria control efforts.