An Evaluation ALgorithms for Classifying Leukocytes Images

摘要

An extremely significant component of the blood that arranges the immune system responsible for combating foreign elements is white blood cells (leukocytes). Neutrophils, eosinophils, lymphocytes, monocytes, and basophils in common are the five specific types of white blood cells. Each class receives a different proportion and typically performs various cognitive functions. It is significant for monitoring patients' health and awareness of the health to be capable of distinguishing and therefore recognizing these various constituents. The discoloration procedure and visual examination for raw images obtained merely are repetitive and based on human errors under the standard microscope. Besides, the distinct lack of training samples investing in white blood cells' spatial differences, such that suitable classification methods can make assumptions effectively, is a significant challenge. Blood cells are correctly examined in a clinical diagnosis through experienced clinicians from potential patients' epithelial cell samples. This considerable research primarily focuses on the morphological characteristics and features of white blood cells and their nuclei and cytoplasm, including forms, proportions, shades, textures, phases of maturity, and staining processes. The proposed method can be appropriately implemented in four stages: segmentation, scanning, feature extraction, and blood cell classification. Next, the cell images are segmented, including the social categorization of white blood cells into specific clusters. The second distinct stage typically includes each segmented image being carefully scanned and the dataset being adequately prepared. The third necessary step is carefully extracting the modern form and complex texture from a scanned image. Typically apply various machine learning algorithms at the final stage (Backpropagation (BP), Multilayer Perceptron (MP), and Support Vector Machine (SVM).