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Microscopic parasite malaria classification using best feature selection based on generalized normal distribution

Javeria Amin1, Muhammad Almas Anjum2, Abraz Ahmad1

  • 1University of Wah, Department of Computer Science, Wah Cantt, Pakistan.

Peerj. Computer Science
|January 10, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel machine learning approach for malaria diagnosis, achieving 99% accuracy in classifying malaria parasites from microscopic images. This method enhances diagnostic efficiency and accuracy, overcoming limitations of traditional microscopy.

Keywords:
EnsembleGNDOKNNMalariaPHOGSVM

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Area of Science:

  • Medical Diagnostics
  • Computational Biology
  • Parasitology

Background:

  • Malaria diagnosis relies on microscopy, which requires specialized skills and can be subjective.
  • Accurate and timely malaria detection is crucial due to its potentially fatal nature.
  • Limitations in current diagnostic methods necessitate advanced, automated solutions.

Purpose of the Study:

  • To develop and evaluate a machine/deep learning-based method for accurate malaria parasite classification.
  • To improve the efficiency and reliability of malaria diagnosis compared to traditional microscopy.
  • To propose a robust feature extraction and selection pipeline for malaria image analysis.

Main Methods:

  • Image enhancement using a bilateral filter.
  • Extraction of shape-based features (Pyramid Histograms of Oriented Gradients - PHOG) and deep features (ResNet-50, ResNet-18).
  • Serial fusion of extracted features followed by feature selection using Generalized Normal Distribution Optimization (GNDO).

Main Results:

  • The proposed method achieved 99% classification accuracy on a microscopic malarial parasite dataset.
  • The integrated feature set (PHOG + ResNet features) demonstrated superior performance.
  • The selected subset of features using GNDO optimized the classification model effectively.

Conclusions:

  • The developed deep learning model offers a highly accurate and efficient alternative for malaria diagnosis.
  • Automated malaria parasite classification can significantly aid in prompt and reliable disease identification.
  • This approach shows promise for improving malaria detection in resource-limited settings.