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September 10, 2025FUDMA Journal of Sciences

Malaria Parasite Detection Using VGG-16

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Authors

AMAbdullahi MayanaNINor Rosidah Ibrahim

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Overview

Deep learning methods improve malaria diagnosis in low-resource settings, suggesting enhanced accuracy and scalability.

Key Points

  • Automated diagnosis using VGG-16 architecture enhances diagnostic efficiency for malaria detection.
  • The approach addresses resource limitations, improving accessibility to malaria diagnostics in affected regions.
  • Transfer learning in convolutional neural networks significantly boosts accuracy compared to traditional techniques.
  • This method could play a crucial role in global efforts to eliminate malaria by streamlining diagnosis.

Cite This Study

Mayana et al. (2025) studied this question.

synapsesocial.com/papers/68c1a25a54b1d3bfb60dd033https://doi.org/10.33003/fjs-2025-0907-3009
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