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Plasmodium parasites are the reason for malaria, a blood-borne illness spread by mosquitoes. The conventional approach to diagnosing malaria is making a blood smear and employing a microscope to appear at the blood-stained smear in order to identify the parasite species Plasmodium. This technique is highly dependent on the knowledge of qualified specialists. Under the cover of this research, shallow machine learning techniques are utilized versus the conventional method, which has certain issues regarding specificity and sensitivity, in order to separate out parasites from blood smears for malaria identification. The suggested methodology uses patient photos to identify the presence of malaria eliminating the requirement for specialists or blood staining.

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An Assessment of Machine Learning Algorithms’ Performance for the Detection of Malaria Using Microscopically Images

  • 会议论文

  • 发表日期：

    2025年

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  • Sheo Kumar,
  • Mohammed Azhar,
  • Banothu Ramji,
  • Uday Kiran Attuluri,
  • D. Muthukrishnan,
  • S. Vaishnavi

摘要

Plasmodium parasites are the reason for malaria, a blood-borne illness spread by mosquitoes. The conventional approach to diagnosing malaria is making a blood smear and employing a microscope to appear at the blood-stained smear in order to identify the parasite species Plasmodium. This technique is highly dependent on the knowledge of qualified specialists. Under the cover of this research, shallow machine learning techniques are utilized versus the conventional method, which has certain issues regarding specificity and sensitivity, in order to separate out parasites from blood smears for malaria identification. The suggested methodology uses patient photos to identify the presence of malaria eliminating the requirement for specialists or blood staining.