Machine Learning Based Blood Cell Categorization in Semar Images

Authors

  • Aniruddha Deshmukhe  Nutan Maharashtra Institute of Engineering and Technology, Talegaon(D), Pune, Maharashtra, India
  • Samarth Dagade  Nutan Maharashtra Institute of Engineering and Technology, Talegaon(D), Pune, Maharashtra, India
  • Nikhil Deshmuksh  Nutan Maharashtra Institute of Engineering and Technology, Talegaon(D), Pune, Maharashtra, India
  • Nilesh Jadhav  Nutan Maharashtra Institute of Engineering and Technology, Talegaon(D), Pune, Maharashtra, India
  • Prof. Ashish Manwatkar  Nutan Maharashtra Institute of Engineering and Technology, Talegaon(D), Pune, Maharashtra, India
  • Nitin Dhawas  Nutan Maharashtra Institute of Engineering and Technology, Talegaon(D), Pune, Maharashtra, India

Keywords:

Deep Learning, Machine Learning, Cell Counting, RBC, WBC

Abstract

The distribution of blood cells in peripheral blood smear (PBS) is important in the diagnosis of blood diseases such as leukemia, anemia, disease, cancer and polycythemia. In blood analysis, hematologists always use a microscope to determine the total number, morphology, and distribution of cells. Hematology analyzers, flow cytometers provide a complete and accurate blood count (CBC) to reveal abnormalities in blood smears. The equipment is expensive, time- consuming [1], requires manual intervention, and is not available in many hospitals. Therefore, a cheap and effective method that can identify different bacteria from a single Periferal Blood Semer Image(PBS) is needed. Automatic classification of samples improves the hematological process, accelerates the diagnostic process and increases the accuracy of the measurement process. The proposed system uses a semi-automatic method to compartmentalize and divide blood cells into white blood cells (WBC) and red blood cells (RBC). The texture of the cells is extracted using Grayscale Co- occurrence Matrix (GLCM) and fed into Logistic Classifiers like Naive Bayes classifier, Knearest neighbor, decision tree, K-means cluster, random forest, regression. , ANN and SVM.

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International Journal of Scientific Research in Science and Technology

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Published

2023-12-30

Issue

Volume 10, Issue 6, November-December-2023

Section

Research Articles

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How to Cite

[1]
Aniruddha Deshmukhe, Samarth Dagade, Nikhil Deshmuksh, Nilesh Jadhav, Prof. Ashish Manwatkar, Nitin Dhawas, " Machine Learning Based Blood Cell Categorization in Semar Images, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 10, Issue 6, pp.255-262, November-December-2023.