White blood cell recognition via geometric features and naïve bays classifier

  • Authors

    • Hasan A. Kazum
    • Faisel G. Mohammed
    https://doi.org/10.14419/ijet.v7i4.21747

  • Blood assessments are of the maximum crucial and frequently asked medical examinations. A manual microscopic evaluation must be done while a blood pattern is suspicious of abnormality. This manual technique is tedious, time ingesting and subjective. Automating microscopic blood type is appropriate to assist the pathologists to hurry-up and induce the consequences accuracy.

    Segmentation is the primary and common step in computerized WBCs category. On this paper, have been presented a powerful method for automated WBCs nuclei segmentation. The technique is based on gray scale contrast enhancement and then using Otsu thresholding tech-nique to segment WBCs. There are four features have found to extract the data from the segmented image. These features are (Area, Perim-eter, diameter, Circularity. Then these data was classified using Naive Bayes classifier under weka program. The approach is examined on 260 blood pictures. The class overall performance is quantitatively evaluated at the take a look at set to be 97,1 %. This overall performance is excessive in comparison to other related work done at the identical dataset.

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  • How to Cite

    Kazum, H. A., & Mohammed, F. G. (2018). White blood cell recognition via geometric features and naïve bays classifier. International Journal of Engineering & Technology, 7(4), 3642-3646. https://doi.org/10.14419/ijet.v7i4.21747