A survey on automated detection and classification of acute leukemia and WBCs in microscopic blood cells

Leukemia (blood cancer) is an unusual spread of White Blood Cells or Leukocytes (WBCs) in the bone marrow and blood. Pathologists can diagnose leukemia by looking at a person’s blood sample under microscope. They identify categorize counting various cells morphological features. This technique time-consuming for prediction leukemia. The pathologist’s professional skills experiences may be affecting this procedure, too. In computer vision, traditional machine learning deep techniques are practical roadmaps that increase accuracy speed diagnosing classifying medical images such as microscopic cells. paper provides comprehensive analysis detection classification acute WBCs First, we have divided previous works into six categories based on output models. Then, describe steps WBCs, including Data Augmentation, Preprocessing, Segmentation, Feature Extraction, Selection (Reduction), Classification, focus step methods. Finally, divide automated three categories, traditional, Deep Neural Network (DNN), mixture (traditional DNN) methods type classifier analyze them. results study show diagnosis Support Vector Machine (SVM) models Convolutional (CNN) widely employed. performance metrics use these classifiers compared to others model higher. We propose providing detecting classify combination SVM CNN their achieve optimum metrics.