dc.contributor.author | Gitonga, Lucy | |
dc.contributor.author | Memeu, Daniel M | |
dc.contributor.author | Kaduki, Kenneth A | |
dc.contributor.author | Mjomba, Allen C K | |
dc.contributor.author | Muriuki, Njogu S | |
dc.date.accessioned | 2015-02-09T13:08:34Z | |
dc.date.available | 2015-02-09T13:08:34Z | |
dc.date.issued | 2014 | |
dc.identifier.citation | Gitonga, L., et al. (2014) Determination of Plasmodium Parasite Life Stages and Species in Images of Thin Blood Smears Using Artificial Neural Network. Open Journal of Clinical Diagnostics, 4, 78-88. | en_US |
dc.identifier.uri | http://dx.doi.org/10.4236/ojcd.2014.42014 | |
dc.identifier.uri | http://hdl.handle.net/11295/80206 | |
dc.description.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. | en_US |
dc.language.iso | en | en_US |
dc.publisher | University of Nairobi | en_US |
dc.title | Determination of plasmodium parasite life stages and species in images of thin blood smears using artificial neural networks | en_US |
dc.type | Article | en_US |
dc.type.material | en_US | en_US |