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Optimized CNN framework for malaria detection using Otsu thresholding-based image segmentation.

Retinderdeep Singh1, Chander Prabha2, Shahab Abdulla3

  • 1Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, 140417, India.

Scientific Reports
|November 17, 2025
PubMed
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This study enhances malaria detection using a deep learning framework with Otsu thresholding segmentation, achieving 97.96% accuracy for early diagnosis from blood smear images.

Area of Science:

  • Medical Imaging
  • Computational Biology
  • Artificial Intelligence

Background:

  • Accurate malaria diagnosis from blood smears is crucial but challenging, especially in resource-limited areas.
  • Current methods often struggle with sensitivity and specificity, impacting patient outcomes.

Purpose of the Study:

  • To develop an optimized deep learning framework for improved malaria-infected cell detection.
  • To enhance diagnostic accuracy by integrating Otsu thresholding-based image segmentation with a convolutional neural network (CNN).

Main Methods:

  • A hybrid parallel feature-fusion model combining a 12-layer CNN and EfficientNet-B7 was developed.
  • Otsu thresholding was applied for image segmentation to emphasize parasite-relevant regions.
  • A dataset of 43,400 blood smear images was used for training and testing, with manual annotation for validation.
Keywords:
Blood cellsCNNClassificationEfficientNetMalariaOtsu’s thresholdSegmentation

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Main Results:

  • The baseline CNN achieved 95% accuracy, improving to 97% with EfficientNet-B7 integration.
  • The Otsu segmentation-enhanced CNN reached a peak accuracy of 97.96%.
  • Segmentation metrics showed high performance (Dice: 0.848, IoU: 0.738), validating Otsu's effectiveness.

Conclusions:

  • Simple preprocessing via Otsu segmentation significantly boosts CNN performance for malaria diagnosis.
  • The proposed framework offers a reliable, scalable, and computationally feasible tool for malaria detection.
  • Segmentation-driven deep learning shows promise for cost-effective malaria diagnostic solutions.