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Automatic Detection and Classification of Malarial Parasite
Muhammad Imran Razzak
Pages - 1 - 12     |    Revised - 01-03-2015     |    Published - 31-03-2015
Volume - 9   Issue - 1    |    Publication Date - March 2015  Table of Contents
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KEYWORDS
Malaria Detection, Segmentation, RBC Classification, Malaria Classification.
ABSTRACT
Recent advancement in genomic technologies has opened a new realm for early detection of diseases that shows potential to overcome the drawbacks of manual detection technologies. Computer based 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 malarial infected cells. Blood cell segmentation and morphological analysis is a challenging due complexity of the blood cells. To improve the performance of malaria parasite segmentation and classification, we have used different set of features which are forward to the ANN for malaria classification. We have used Rao’s method and bounding box for segmentation whereas we have used BPNN for classification on different set of texture and shape features.
CITED BY (2)  
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Dr. Muhammad Imran Razzak
KSAU-HS - Saudi Arabia
razzakmu@ngha.med.sa