DIAGNOSIS OF MICROSCOPIC BLOOD SAMPLES FOR EARLY DETECTION OF WHITE BLOOD CELL DISEASES

D.E Eshmurodov; F.K Tulaganova

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D.E Eshmurodov Muallif
F.K Tulaganova Muallif

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X-ray films##common.commaListSeparator## Machine Learning##common.commaListSeparator## diagnosis##common.commaListSeparator## performance##common.commaListSeparator## algorithms##common.commaListSeparator## differentia##common.commaListSeparator## dental panoramic

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The immune system is one of the most important human systems, it fights against all diseases and protects the body from viruses, bacteria, etc. White blood cells (WBC) play an important role in the immune system. To diagnose blood disorders, doctors analyse blood samples to describe the characteristics of WBCs. The characteristics of WBC are determined based on the chromatic, geometric and textural characteristics of the WBC nucleus. Manual diagnosis is subject to many errors and different opinions of experts and takes a long time; however, artificial intelligence techniques can help solve all these problems. Automatic diagnosis of WBC type helps hematologists to identify different types of blood disorders. This work aims to overcome manual diagnosis by developing automated systems for classification of microscopic blood sample datasets for early detection of WBC diseases. Several proposed systems have been used.

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