Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
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.
"Automated Enumeration of Malaria Parasite Using SVM Classifier", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.3, Issue 10, page no.159 - 165, October-2018, Available :http://www.ijrti.org/papers/IJRTI1810027.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator