Unsupervised Learning-Based Feature Engineering in Malaria Cell Recognition

Authors

  • Dr. K. Sravan Kumar, Punamalli Chaitanya Kumar, Ramireddy Gari Gangi Reddy, Peram Suresh

Keywords:

Malaria Detection, Deep Learning, Image Processing, Automated Diagnosis, Unsupervised Feature Extraction, Blood Smear Analysis, Portable Diagnostic Tools, Medical Image Classification, Plasmodium, Machine Learning in Healthcare.

Abstract

Malaria, a potentially fatal disease caused by Plasmodium parasites and transmitted via the bites ofinfected mosquitoes, continues to pose a serious public health threat across many parts of the world.Early and precise diagnosis is essential for effective treatment and disease control. Automated malaria

References

. WHO. World Malaria Report 2022. Available online: https://www.who.int/teams/globalmalaria-programme/reports/world-malaria-report-2022 (accessed on 1 March 2023). Trends.

. WHO. World Malaria Report 2021: An In-Depth Update on Global and Regional Malaria Data and Available

online: https://www.who.int/teams/global-malariaprogramme/reports/world-malaria-report-2021 (accessed on 1 September 2022).

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Published

2025-04-15

How to Cite

Dr. K. Sravan Kumar, Punamalli Chaitanya Kumar, Ramireddy Gari Gangi Reddy, Peram Suresh. (2025). Unsupervised Learning-Based Feature Engineering in Malaria Cell Recognition . Journal of Computational Analysis and Applications (JoCAAA), 34(4), 1233–1241. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/3118

Issue

Vol. 34 No. 4 (2025): JOCAAA

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.