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April 16, 2026International Journal of Engineering & TechnologyOpen Access

White blood cell recognition via geometric features and naïve bays classifier

Hasan A. Kazum Faisel G. Mohammed

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Key Points

The study aims to automate the recognition of white blood cells by using geometric features and machine learning.
Implemented a WBCs nuclei segmentation method using gray scale contrast enhancement and Otsu thresholding.
Extracted features such as area, perimeter, diameter, and circularity from segmented images.
Classified the features using the Naive Bayes classifier in the Weka program.
Achieved a classification performance of 97.1% on a dataset of 260 blood images.
Demonstrated higher accuracy compared to previous methods on the same dataset.

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Implication

Research demonstrates high accuracy in classifying white blood cells using automated segmentation and machine learning.

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Cite This Study

Kazum et al. (2026) studied this question.

synapsesocial.com/papers/69e7138bcb99343efc98cfaa https://doi.org/10.14419/ijet.v7i4.21747

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