Detection of Malarial Parasites Using Image Processing with Machine Learning and Deep Learning |
Author(s): |
Suyesha Lamne , Ramrao adik Institute of Technology; Prakhar Pandey, Ramrao adik Institute of Technology |
Keywords: |
Image Processing, Machine Learning, Deep Learning |
Abstract |
Malaria is a major issue faced on the global level. More than 400,000 deaths per year are caused by Malaria. Biomedical research and steps are taken by the government; technology is playing a crucial role in curbing this fatal disease. One of the biggest barriers to the increasing death rate is the lack of malaria identification techniques. To improve the identification and to reduce the time taken to detect the bacteria affecting on a large scale, image analysis software and machine learning methods are emerging to evaluate parasite-affected blood in the microscopic blood slides. According to the traditional method, for detecting malaria is done by taking the infected blood cell of the patient is placed on a slide and is observed under a microscope. The examination involving an expert technician with his intense visual examines the slide and counts the infected RBCs. This is an extremely laborious and tedious process. We will generate a new image processing system for the detection and analysis of plasmodium bacteria in the blood smear slide. We further develop a Machine Learning algorithm to learn, detect and determine the types of infected cells according to its feature. |
Other Details |
Paper ID: IJSRDV10I60099 Published in: Volume : 10, Issue : 6 Publication Date: 01/09/2022 Page(s): 154-158 |
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