Comparison of traditional image processing and deep learning approaches for classification of white blood cells in peripheral blood smear images

Roopa B. Hegde, Keerthana Prasad, Harishchandra Hebbar, Brij Mohan Kumar Singh

Research output: Contribution to journalArticle

Abstract

Automated classification and morphological analysis of white blood cells has been addressed since last four decades, but there is no optimal method which can be used as decision support system in laboratories due to biologically complex nature of the cells. Automated blood cell analysis facilitates quick and objective results and can also handle massive amount of data without compromising with efficiency. In the present study, we demonstrate classification of white blood cells into six types namely lymphocytes, monocytes, neutrophils, eosinophils, basophils and abnormal cells. We provide the comparison of traditional image processing approach and deep learning methods for classification of white blood cells. We evaluated neural network classifier results for hand-crafted features and obtained the average accuracy of 99.8%. We also used full training and transfer learning approaches of convolutional neural network for the classification. An accuracy around 99% was obtained for full training CNN.

Original languageEnglish
Pages (from-to)382-392
Number of pages11
JournalBiocybernetics and Biomedical Engineering
Volume39
Issue number2
DOIs
Publication statusPublished - 01-04-2019

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Image processing
Blood
Cells
Neural networks
Lymphocytes
Decision support systems
Classifiers
Deep learning

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering

Cite this

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abstract = "Automated classification and morphological analysis of white blood cells has been addressed since last four decades, but there is no optimal method which can be used as decision support system in laboratories due to biologically complex nature of the cells. Automated blood cell analysis facilitates quick and objective results and can also handle massive amount of data without compromising with efficiency. In the present study, we demonstrate classification of white blood cells into six types namely lymphocytes, monocytes, neutrophils, eosinophils, basophils and abnormal cells. We provide the comparison of traditional image processing approach and deep learning methods for classification of white blood cells. We evaluated neural network classifier results for hand-crafted features and obtained the average accuracy of 99.8{\%}. We also used full training and transfer learning approaches of convolutional neural network for the classification. An accuracy around 99{\%} was obtained for full training CNN.",
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Comparison of traditional image processing and deep learning approaches for classification of white blood cells in peripheral blood smear images. / Hegde, Roopa B.; Prasad, Keerthana; Hebbar, Harishchandra; Singh, Brij Mohan Kumar.

In: Biocybernetics and Biomedical Engineering, Vol. 39, No. 2, 01.04.2019, p. 382-392.

Research output: Contribution to journalArticle

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