Machine learning (ML) and deep learning (DL) models have been employed to significantly improve analyses of medical imagery, with these approaches used to enhance the accuracy of prediction and classification. Model predictions and classifications assist diagnoses of various cancers and tumors. This review presents an in-depth analysis of modern techniques applied within the domain of medical image analysis for white blood cell classification. The methodologies that use blood smear images, magnetic resonance imaging (MRI), X-rays, and similar medical imaging domains are identified and discussed, with a detailed analysis of ML/DL techniques applied to the classification of white blood cells (WBCs) representing the primary focus of the review. The data utilized in this research has been extracted from a collection of 136 primary papers that were published between the years 2006 and 2023. The most widely used techniques and best-performing white blood cell classification methods are identified. While the use of ML and DL for white blood cell classification has concurrently increased and improved in recent year, significant challenges remain - 1) Availability of appropriate datasets remain the primary challenge, and may be resolved using data augmentation techniques. 2) Medical training of researchers is recommended to improve current understanding of white blood cell structure and subsequent selection of appropriate classification models. 3) Advanced DL networks including Generative Adversarial Networks, R-CNN, Fast R-CNN, and faster R-CNN will likely be increasingly employed to supplement or replace current techniques.

翻译：机器学习（ML）与深度学习（DL）模型已被广泛用于显著提升医学影像分析的质量，这些方法被用于提高预测与分类的准确性。模型预测与分类有助于各类癌症及肿瘤的诊断。本综述深入分析了现代技术在医学图像分析领域（特别是白细胞分类）中的应用。研究识别并讨论了使用血涂片图像、磁共振成像（MRI）、X射线等医学影像模态的方法，并重点详细分析了应用于白细胞分类的ML/DL技术。本研究的数据来源于2006年至2023年间发表的136篇核心论文。综述确定了最广泛使用的技术以及性能最佳的白细胞分类方法。尽管近年来ML和DL在白细胞分类中的应用同步增长并不断改进，但仍存在重大挑战：1）合适数据集的可用性仍是首要挑战，可通过数据增强技术加以解决。2）建议对研究人员进行医学培训，以提升对白细胞结构的理解，进而选择合适的分类模型。3）先进的DL网络（包括生成对抗网络、R-CNN、Fast R-CNN和Faster R-CNN）将越来越多地被用于补充或替代现有技术。