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Zhou, M., Liu, Q., Wongsrichanalai, C., Suwonkerd, W., Panart, K., Prajakwong, S., Pensir, A., Kimura, M., Atsuoka, H., Ferreira, M.U., Isomura, S. and Kawamoto, F. (1998) High Prevalence of Plasmodium malariae and Plasmodium ovale in Malaria Patients along the Thai-Myanmar Border, as Revealed by Acridine Orange Staining and PCR-Based Diagnoses. Tropical Medicine and International Health.

has been cited by the following article:

• 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.