Description
Title: White blood cell objective assessment using novel computational vision algorithms
Abstract: One of the most crucial tests in medicine to ascertain a patient’s physiological state is a blood analysis. The manual counting and classification of white blood cells, which is a crucial step in the analysis of a blood sample, is labor-intensive, subject to human error, and necessitates a professional to be physically exhausted because it takes a lot of time. The goal of the study was to create a system for identifying and categorizing blood cells using networks of Gaussian radial base functions (RBFN) to extract the nucleus, followed by classification based on morphological traits, color, and proximity to other objects. Last but not least, the results from the validation using the coefficient of determination demonstrated an overall accuracy of 97.9% in the classification of white blood cells per individual, while the accuracy in the classification by type of cell evidenced results in 93.4% for lymphocytes, 97.37% for monocytes, 79.5% for neutrophils, 73.07% for eosinophils, and a 100% in basophils with respect to the professional. By improving the analysis for identifying blood cells in this way, the suggested system transforms into a dependable technological aid that benefits both the processes of medical research as well as low-level hematology establishments.
Paper Quality: SCOPUS / Web of Science Level Research Paper
Subject: Medicine
Sub Category: Hematology
Writer Experience: 20+ Years
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