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

翻译：白血病（血液癌症）是骨髓和血液中白细胞异常增殖的疾病。病理学家通过显微镜观察血液样本诊断白血病，依据各类血细胞计数及形态学特征进行识别与分类。该方法耗时且易受病理学家专业技能与经验的影响。在计算机视觉领域，传统机器学习和深度学习技术为解决医学图像（如显微镜血细胞）的诊断与分类提供了切实可行的方案，能有效提升准确率与速度。本文全面分析了显微镜血细胞中急性白血病与白细胞检测与分类的研究现状。首先，基于模型输出结果将既往研究划分为六类；继而阐述了检测与分类流程中的各环节，包括数据增强、预处理、分割、特征提取、特征选择（降维）、分类，并重点聚焦于分类方法；最后，依据分类步骤中使用的分类器类型，将急性白血病与白细胞的自动检测与分类方法分为三类：传统方法、深度神经网络方法和混合方法（传统与深度神经网络结合），并对其进行深入分析。研究结果表明，在急性白血病与白细胞的诊断与分类中，支持向量机分类器（传统机器学习模型）与卷积神经网络分类器（深度学习模型）被广泛采用，采用这两种分类器的模型性能指标显著优于其他模型。