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International Journal for Research Trends and Innovation
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Volume 3 | Issue 11

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Paper Title: Automated Enumeration of Malaria Parasite Using SVM Classifier
Authors Name: Sayali N. Dharpal , Dr.A.V. Malviya
Unique Id: IJRTI1810027
Published In: Volume 3 Issue 10, October-2018
Abstract: The current highest standard level of malaria diagnosis is the manual, microscopy-based analysis of stained blood smears or in other words devouring procedure requiring skilled specialists. This paper introduces a algorithm that recognizes and counts red platelets (RBCs) and additionally stained parasites with the end goal to perform a computation of malaria parasite. This proposed framework help to build up a totally automated system for classification of malaria parasite contaminated erythrocytes are segmented from the pre-processed images. Statistical and color features are extracted and given to the SVM binary classifier which describes Malaria infected erythrocytes on blood smears. The outcomes indicate 98.98% sensitivity and 97.02% accuracy for detecting infected red blood cells.
Keywords: Malaria, Parasite, erythrocyte, Feature Extraction, SVM classifier.
Cite Article: "Automated Enumeration of Malaria Parasite Using SVM Classifier", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.3, Issue 10, page no.159 - 165, October-2018, Available :http://www.ijrti.org/papers/IJRTI1810027.pdf
Downloads: 000645
Publication Details: Published Paper ID: IJRTI1810027
Registration ID:180537
Published In: Volume 3 Issue 10, October-2018
DOI (Digital Object Identifier):
ISSN Number: 2456 - 3315
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