Malaria Detection Using Image Processing and Machine Learning

TL;DR

This paper presents a new image processing system combined with machine learning algorithms to automate and improve the detection and classification of malaria-infected blood cells, replacing manual microscopy.

Contribution

It introduces a novel image processing and machine learning framework for malaria diagnosis, enhancing accuracy and efficiency over traditional manual methods.

Findings

Automated detection of infected cells achieved high accuracy.

Machine learning models effectively classify parasite types.

System reduces diagnostic time compared to manual microscopy.

Abstract

Malaria is mosquito-borne blood disease caused by parasites of the genus Plasmodium. Conventional diagnostic tool for malaria is the examination of stained blood cell of patient in microscope. The blood to be tested is placed in a slide and is observed under a microscope to count the number of infected RBC. An expert technician is involved in the examination of the slide with intense visual and mental concentration. This is tiresome and time consuming process. In this paper, we construct a new mage processing system for detection and quantification of plasmodium parasites in blood smear slide, later we develop Machine Learning algorithm to learn, detect and determine the types of infected cells according to its features.