Comparative Analysis of White Blood Cell Segmentation: HSV Color Space Model vs Traditional Machine Learning Techniques

White blood cell (WBC) analysis plays a crucial role in diagnosing and treating various diseases. The research utilizes the HSV color space technique along with conventional machine learning approaches, including the Random Forest Classifier and Support Vector Machine (SVM), to differentiate various kinds of white blood cells (WBCs) and measure both their quantity and area. The segmentation results of these three methods are compared, and it is observed that the HSV color space model efficiently segments the region of interest of blood cells. In the context of traditional machine learning, the segmentation of white blood cells (WBCs) is evaluated using metrics such as the Dice Coefficient and the Jaccard Index. According to the findings, the Random Forest model attained a Dice Coefficient score of 90.4% and a Jaccard Index score of 82.5%. In contrast, the Support Vector Machine (SVM) model produced a Dice Coefficient score of 79.7% and a Jaccard Index score of 66.3%. The proposed methods demonstrate high accuracy and effectively segment diverse types and shapes of white blood cells. Furthermore, they facilitate the analysis of a significant numbers of white blood cells within a brief timeframe.