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Barnacles Mating Optimizer with Deep Transfer Learning Enabled Biomedical Malaria Parasite Detection and

Ashit Kumar Dutta1, R Uma Mageswari2, A Gayathri3

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Summary
This summary is machine-generated.

This study introduces a novel deep learning model for automated malaria parasite detection and classification. The Barnacles Mating Optimizer with Deep Transfer Learning Enabled Biomedical Malaria Parasite Detection and Classification (BMODTL-BMPC) model significantly improves diagnostic accuracy.

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Area of Science:

  • Biomedical Engineering
  • Computational Intelligence
  • Deep Learning

Background:

  • Malaria diagnosis is critical but manual methods are time-consuming.
  • Automated diagnostic tools using computational intelligence (CI) are gaining interest.
  • Deep learning (DL) methods offer potential for enhanced diagnostic accuracy over traditional handcrafted features.

Purpose of the Study:

  • To introduce an intelligent model for malaria parasite recognition and classification.
  • To develop a novel Barnacles Mating Optimizer with Deep Transfer Learning Enabled Biomedical Malaria Parasite Detection and Classification (BMODTL-BMPC) model.
  • To improve the accuracy and efficiency of malaria diagnosis.

Main Methods:

  • Gaussian filtering (GF) for noise reduction in blood smear images.
  • Graph cuts (GC) segmentation to identify affected regions.
  • Barnacles mating optimizer (BMO) algorithm combined with NasNetLarge for feature extraction.
  • Extreme learning machine (ELM) for parasite classification.

Main Results:

  • The BMODTL-BMPC model demonstrated superior performance in malaria parasite detection and classification.
  • Experimental analysis on a benchmark dataset confirmed the model's effectiveness.
  • The proposed technique outperformed existing state-of-the-art approaches.

Conclusions:

  • The BMODTL-BMPC model offers an effective automated solution for malaria diagnosis.
  • The integration of deep transfer learning and optimization algorithms enhances diagnostic capabilities.
  • This approach holds promise for clinical applications in malaria detection.