Abstract
The counts of various types of white blood cells give vital information for identifying a variety of ailments utilizing the video internet of things (VIoT). This technique needs to be automated to save time and minimize counting errors. This paper aims to apply recent image processing techniques to identify and categorize white blood cells (WBCs) found in peripheral blood and to develop a system that would automatically detect and evaluate WBCs. The proposed method consists of steps such as segmentation scanning, feature extraction, and blood cell categorization. First, we segment the cell pictures, which entails grouping white blood cells into clusters. The second stage consists of scanning each input image and preparing the dataset. The third stage involves determining the texture and contour of a scanned picture. Finally, different machine learning algorithms are used to classify the results based on these criteria.
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