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Blood and its components have an important place in human life and are the best indicator tool in determining many pathological conditions.In particular, the classification of white blood cells is of great importance for the diagnosis of hematological diseases. In this study, hundreds of microscopic blood images were tested with 6 different machine learning algorithms for the classification of white blood cells and their performances were compared .According to the results, the Random Forest algorithm has performed better than the other methods with an average 80% test success.
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Cite Article:
"Classifying White Blood Cells and Comparison Of Different Algorithms Using Machine Learning", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 5, page no.385 - 392, May-2023, Available :http://www.ijrti.org/papers/IJRTI2305058.pdf
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ISSN:
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