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Leukemia is a blood-forming cancer disease characterized by unusual white blood Cell proliferation. Early detection and treatment of cancer reduce mortality and survival rates, raising the risk of death and increasing the survival rate. The majority of the k-NN approaches used a mix of mathematical and geometrical characteristics of the nucleus and cytoplasm with and without normalization, resulting in an inconsistent array of features. To solve the issue, this paper introduces a Z-Score standardized feature set in conjunction with a k-NN maximum distance measurement-based automated blood cancer diagnosis device.
In this paper, the cancer-affected region of white blood cell photos is investigated. Different types of white blood cells can be classified by AlexNet's architecture to distinguish various types of white blood cells. To enhance the quality and accuracy of blood cancer detection, various stages of image processing are used. The severity of the blood sample image is determined by the geographic location.
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