Blood Cell Image Classification Using the Machine Learning Methods With Nature Inspired Optimization
Keywords:
Blood cell, image classification, Enhanced Naive Bayes Ant Colony Optimization (ENBACO)Abstract
Blood cells are the biological components that make up our bodies' blood. They are essential in a variety of physiological activities like oxygen transport, immunological response, and coagulation. There is theoretical as well as practical interest in the issue of detecting and counting blood cells inside the blood smear. For the diagnosis and treatment of many disorders, the differentiating blood cell count offers pathologists vital information. In this study, we offer integrated of Enhanced Naive Bayes Ant Colony Optimization (ENACO) for efficiently classifying and identifying blood cell images. The most crucial steps in this automated procedure are segmentation and classification of blood cells. To detect and categorize the various kinds of blood cells, Digital microscopic pictures of the stained blood cells are segmented, and geometrical characteristics are extracted for each segment. The Enhanced Naive Bayes-Ant Colony Optimization (ENBACO) was suggested. The experimental findings are compared with the researchers' other current approaches, demonstrating the efficiency of the suggested strategy. Typical assessment metrics include F1-score, recall, accuracy, and precision. The suggested results were superior to other traditional procedures.
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