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INTERNATIONAL JOURNAL OF CREATIVE RESEARCH THOUGHTS - IJCRT (IJCRT.ORG)
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ISSN Approved Journal No: 2320-2882 | Impact factor: 7.97 | ESTD Year: 2013
Call For Paper - Volume 12 | Issue 7 | Month- July 2024
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Published Paper Details:
Paper Title
Machine Learning-Based Blood Cell Categorization In Smear Images
Authors
Rohit Sonawane, Manisha Darak, Sai Yandralwar, Abdul Shaikh
Keywords
Blood Cell Categorization, Medical Imaging, Image Processing, Smear Images, Machine Learning
Abstract
This research focuses on utilizing machine
learning techniques to automate the classification of
blood cells into white blood cells (WBCs) and red blood
cells (RBCs) from blood smear images. The process
involves data preprocessing, feature extraction or deep
learning, model training, and evaluation. Various
models are explored, including traditional classifiers
and convolutional neural networks (CNNs).The model's
performance is assessed using standard binary
classification metrics. Successful models can be
deployed in healthcare for efficient blood cell
classification, contributing to streamlined diagnostics.
Ethical considerations regarding data privacy are
integral to this process. This research aims to provide a
valuable tool for healthcare professionals, enhancing
the speed and accuracy of blood cell analysis.
Keywords - Blood Cell Categorization, Machine
Learning , Smear Images, CNN , Image Processing.
IJCRT's Publication Details
Unique Identification Number - IJCRT2312386
Paper ID - 247721
Page Number(s) - d432-d437
Pubished in - Volume 11 | Issue 12 | December 2023
DOI (Digital Object Identifier) -
Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
Cite this article
Rohit Sonawane, Manisha Darak, Sai Yandralwar, Abdul Shaikh, "Machine Learning-Based Blood Cell Categorization In Smear Images", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.11, Issue 12, pp.d432-d437, December 2023, Available at :http://www.ijcrt.org/papers/IJCRT2312386.pdf
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Volume 12 | Issue 7
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
ISSN and 7.97 Impact Factor Details
ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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