TITLE:
Determination of Plasmodium Parasite Life Stages and Species in Images of Thin Blood Smears Using Artificial Neural Network
AUTHORS:
Lucy Gitonga, Daniel Maitethia Memeu, Kenneth Amiga Kaduki, Mjomba Allen Christopher Kale, Njogu Samson Muriuki
KEYWORDS:
Plasmodium, Artificial Neural Network (ANN), Classifier, RGB, Train Set, Target
JOURNAL NAME:
Open Journal of Clinical Diagnostics,
Vol.4 No.2,
June
4,
2014
ABSTRACT: Malaria is a leading cause of deaths globally. Rapid and accurate diagnosis
of the disease is key to its effective treatment and management. Identification
of plasmodium parasites life stages and species forms part of the diagnosis. In
this study, a technique for identifying the parasites life stages and species using
microscopic images of thin blood smears stained with Giemsa was developed. The technique
entailed designing and training Artificial Neural Network (ANN) classifiers to perform
the classification of infected erythrocytes into their respective stages and species.
The outputs of the system were compared to the results of expert microscopists.
A total of 205 infected erythrocytes images were used to train and test the performance
of the system. The system recorded 99.9% in recognizing stages and 96.2% in recognizing
plasmodium species.