Malaria Detection Using Image Processing and Machine Learning
This addresses the time-consuming and labor-intensive task of malaria detection for medical technicians, but appears incremental as it builds on existing image processing and machine learning approaches.
The paper tackles the problem of manual malaria diagnosis by developing an image processing system and machine learning algorithm to detect and quantify Plasmodium parasites in blood smear slides, aiming to automate the process.
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.