Review of Microscopic Image Processing techniques towards Malaria Infected Erythrocyte Detection from Thin Blood Smears
Review of Microscopic Image Processing techniques towards Malaria Infected Erythrocyte Detection from Thin Blood Smears
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Keywords

texture features
soft computing
morphological features
microscopic image processing
malaria parasite
malaria detection
machine learning
heurist

How to Cite

Md. Jaffar Sadiq. (2017). Review of Microscopic Image Processing techniques towards Malaria Infected Erythrocyte Detection from Thin Blood Smears. Global Journal of Computer Science and Technology, 17(F2), 7–13. Retrieved from https://gjcst.com/index.php/gjcst/article/view/666

Abstract

In order to diagnose malaria the test that has traditionally been conducted is the gold standard test The process mainly entails the preparation of a blood smear on glass slide staining the blood and examining the blood through the use of a microscope so as to observe parasite genus plasmodium Although these are several other kinds of diagnostic test solutions that are available and which can be adopted there are numerous shortcomings which are always observed when microscopic analysis is carried out Presently the treatments are hugely conducted based on symptoms and upon the occurrence of false negatives it might be fatal and may result into the creation of different kinds of implications There have been a number of deaths which have been associated with malaria and as a result there is the dire need to ensure that there is early detection of malarial infection among the people This manuscript mainly provides a review of the current contributions regarding computer aided strategies as well as microscopic image processing strategies for the detection of malaria They are discussed based on the contemporary literature
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