AUTOMATED MALARIA DETECTION: LEVERAGING MACHINE LEARNING FOR ENHANCED DIAGNOSTIC ACCURACY

Authors:

Anusha Chamanthi, Mounika Valavoju, Chigurlapalli Swathi

Page No: 1642-1658

Abstract:

In many parts of the world, malaria—a potentially fatal illness brought on by Plasmodium parasites spread by infected mosquitoes—remains a major public health problem. For prompt medical intervention and effective illness management, early and precise identification of malaria infection is essential. Healthcare practitioners may conduct quick and precise malaria tests in distant or resource-constrained environments by integrating the automated malaria detection system into portable diagnostic instruments. The method can help epidemiological studies and effective resource allocation by helping academics and health organizations track the prevalence of malaria and monitor its spread.

Description:

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Volume & Issue

Volume-12,Issue-4

Keywords

Keywords: Random Forest Classifier, Image Preprocessing, and Maleria Detection