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

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....

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Publicado en:Journal of Digital Imaging Vol. 33; no. 2; pp. 361 - 375
Autores principales: Hegde, Roopa B., Prasad, Keerthana, Hebbar, Harishchandra, Singh, Brij Mohan Kumar, Sandhya, I
Formato: diagnostic images pictorial research tables/charts Journal Article
Publicado: Springer Nature Apr2020
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Apr2020
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s10278-019-00288-y
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        atl: Automated Decision Support System for Detection of Leukemia from Peripheral Blood Smear Images.
      aug:
        au:
          Hegde, Roopa B.
          Prasad, Keerthana
          Hebbar, Harishchandra
          Singh, Brij Mohan Kumar
          Sandhya, I
        affil: Manipal School of Information Sciences, MAHE, 576104, Manipal, India
      sug:
        subj:
          Automation
          Decision Support Systems, Clinical
          Image Processing, Computer Assisted
          Hematologic Tests
          Leukemia Diagnosis
          Human
          Staining and Labeling
          Leukocyte Count
          Validity
      ab: 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.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        pictorial
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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