Malaria Parasite Detection using Various Machine Learning Algorithms and Image Processing

International Journal of Computer Science and Engineering

© 2020 by SSRG - IJCSE Journal

Volume 7 Issue 2

Year of Publication : 2020

Authors : Yash Panchori, Nikita Agarwal, Aashiya Singhal, Sudhir Busa

10.14445/23488387/IJCSE-V7I2P108

How to Cite?

Yash Panchori, Nikita Agarwal, Aashiya Singhal, Sudhir Busa, "Malaria Parasite Detection using Various Machine Learning Algorithms and Image Processing," SSRG International Journal of Computer Science and Engineering , vol. 7,  no. 2, pp. 68-70, 2020. Crossref, https://doi.org/10.14445/23488387/IJCSE-V7I2P108

Abstract:

Malaria is mosquito-borne blood disease caused by protozoan parasites of the genus Plasmodium. The Conventional diagnostic tool for malaria is the examination of a stained blood cell of a patient in microscope which is time consuming and dependent on the experience of a pathologist. In this project, an improved image processing system along with different machine learning algorithms for detection of parasites is proposed. On implementation we found the accuracy of the model varying from 85% to 90% for different algorithms. This model has increased the efficiency of malaria parasite detection and minimizes the human intervention during the detection process.

Keywords:

machine learning, image processing, malaria, Gaussian blur, classification, contour, regression

References:

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