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Machine learning (ML) and deep learning (DL) models have been employed tosignificantly improve analyses of medical imagery, with these approaches usedto enhance the accuracy of prediction and classification. Model predictions andclassifications assist diagnoses of various cancers and tumors. This reviewpresents an in-depth analysis of modern techniques applied within the domain ofmedical image analysis for white blood cell classification. The methodologiesthat use blood smear images, magnetic resonance imaging (MRI), X-rays, andsimilar medical imaging domains are identified and discussed, with a detailedanalysis of ML/DL techniques applied to the classification of white blood cells(WBCs) representing the primary focus of the review. The data utilized in thisresearch has been extracted from a collection of 136 primary papers that werepublished between the years 2006 and 2023. The most widely used techniques andbest-performing white blood cell classification methods are identified. Whilethe use of ML and DL for white blood cell classification has concurrentlyincreased and improved in recent year, significant challenges remain - 1)Availability of appropriate datasets remain the primary challenge, and may beresolved using data augmentation techniques. 2) Medical training of researchersis recommended to improve current understanding of white blood cell structureand subsequent selection of appropriate classification models. 3) Advanced DLnetworks including Generative Adversarial Networks, R-CNN, Fast R-CNN, andfaster R-CNN will likely be increasingly employed to supplement or replacecurrent techniques.

Classification of White Blood Cells Using Machine and Deep Learning Models: A Systematic Review