  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.
