Automated Decision Support System for Detection of Leukemia from Peripheral Blood Smear Images

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

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)


Peripheral blood smear analysis plays a vital role in diagnosing many diseases including cancer. Leukemia is a type of cancer which begins in bone marrow and results in increased number of white blood cells in peripheral blood. Unusual variations in appearance of white blood cells indicate leukemia. In this paper, an automated method for detection of leukemia using image processing approach is proposed. In the present study, 1159 images of different brightness levels and color shades were acquired from Leishman stained peripheral blood smears. SVM classifier was used for classification of white blood cells into normal and abnormal, and also for detection of leukemic WBCs from the abnormal class. Classification of the normal white blood cells into five sub-types was performed using NN classifier. Overall classification accuracy of 98.8% was obtained using the combination of NN and SVM.

Original languageEnglish
Pages (from-to)361-374
Number of pages14
JournalJournal of Digital Imaging
Issue number2
Publication statusPublished - 01-04-2020

All Science Journal Classification (ASJC) codes

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications


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