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

arXiv: https://arxiv.org/abs/2308.06296 · HTML: https://mlsanity.com/paper/2308.06296/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 114 references that are in the map.
- First posted on arXiv 11 Aug 2023.

## Builds on (most cited first, 8 of 8)

- [Understanding deep learning requires rethinking generalization](https://mlsanity.com/paper/1611.03530/) (2016)
- [NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding](https://mlsanity.com/paper/1905.04757/) (2019)
- [Deep Semantic Segmentation of Natural and Medical Images: A Review](https://mlsanity.com/paper/1910.07655/) (2019)
- [Deep Learning for Medical Image Processing: Overview, Challenges and Future](https://mlsanity.com/paper/1704.06825/) (2017)
- [Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions](https://mlsanity.com/paper/1902.05655/) (2019)
- [Applying Faster R-CNN for Object Detection on Malaria Images](https://mlsanity.com/paper/1804.09548/) (2018)
- [A Hybrid Deep Learning Architecture for Leukemic B-lymphoblast Classification](https://mlsanity.com/paper/1909.11866/) (2019)
- [A novel algorithm for segmentation of leukocytes in peripheral blood](https://mlsanity.com/paper/1905.08416/) (2019)

## Built on by (most cited first, 4 of 4)

- [Deep Generative Classification of Blood Cell Morphology](https://mlsanity.com/paper/2408.08982/) (2024)
- [Mamba-Based Ensemble learning for White Blood Cell Classification](https://mlsanity.com/paper/2504.11438/) (2025)
- [Generalizable Blood Cell Detection via Unified Dataset and Faster R-CNN](https://mlsanity.com/paper/2511.08465/) (2025)
- [Enhancing Blood Cells Classification using Hybrid Quantum Neural Networks](https://mlsanity.com/paper/2605.23324/) (2026)

## Abstract (arXiv)

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.