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Malarial Parasite Classification using Recurrent Neural Network
Imran Razzak
Pages - 69 - 79     |    Revised - 01-03-2015     |    Published - 31-03-2015
Volume - 9   Issue - 2    |    Publication Date - March / April 2015  Table of Contents
Malaria Detection, Segmentation, RBC Classification, Blood Cell Analysis.
Malaria parasite detection relies mainly on the manual examination of Giemsa-stained blood microscopic slides whereas it is very long, tedious, and prone to error. Automatic malarial parasite analysis and classification has opened a new area for the early malaria detection that showed potential to overcome the drawbacks of manual strategies. This paper presented a method for automatic detection of falciparum and vivax plasmodium. Although, malaria cell segmentation and morphological analysis is a challenging problem due to both the complex cell nature uncertainty in microscopic videos. To improve the performance of malaria parasite segmentation and classification, segmented the RBC and used RNN for classification into its type. Segmented RBCs are classified into normal RBC and infected cell. RNN identify the infected cells into further types.
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Dr. Imran Razzak
KSAU-HS - Saudi Arabia