Feature extraction using traditional image processing and convolutional neural network methods to classify white blood cells: a study

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

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White blood cells play a vital role in monitoring health condition of a person. Change in count and/or appearance of these cells indicate hematological disorders. Manual microscopic evaluation of white blood cells is the gold standard method, but the result depends on skill and experience of the hematologist. In this paper we present a comparative study of feature extraction using two approaches for classification of white blood cells. In the first approach, features were extracted using traditional image processing method and in the second approach we employed AlexNet which is a pre-trained convolutional neural network as feature generator. We used neural network for classification of WBCs. The results demonstrate that, classification result is slightly better for the features extracted using the convolutional neural network approach compared to traditional image processing approach. The average accuracy and sensitivity of 99% was obtained for classification of white blood cells. Hence, any one of these methods can be used for classification of WBCs depending availability of data and required resources.

Original languageEnglish
Pages (from-to)627-638
Number of pages12
JournalAustralasian Physical and Engineering Sciences in Medicine
Issue number2
Publication statusPublished - 15-06-2019


All Science Journal Classification (ASJC) codes

  • Biophysics
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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