Classification of white blood cells using weighted optimized deformable convolutional neural networks

Abstract

Background

Machine learning (ML) algorithms have been widely used in the classification of white blood cells (WBCs). However, the performance of ML algorithms still needs to be addressed for being short of gold standard data sets, and even the implementation of the proposed algorithms.

Methods

In this study, the method of two-module weighted optimized deformable convolutional neural networks (TWO-DCNN) was proposed for WBC classification. Our algorithm is characterized as two-module transfer learning and deformable convolutional (DC) layers for the betterment of robustness. To validate the performance, our method was compared with classical MLs of VGG16, VGG19, Inception-V3, ResNet-50, support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT) and random forest (RF) on our undisclosed WBC data set and public BCCD data set.

Results

TWO-DCNN achieved the best performance with the precisions (PREs) of 95.7%, 94.5% and 91.6%, recalls (RECs) of 95.7%, 94.5% and 91.6%, F1-scores (F1s) of 95.7%, 94.5% and 91.6%, area under curves (AUCs) of 0.98, 0.97 and 0.95 for low-resolution and noisy undisclosed data sets, BCCD data set, respectively.

Conclusions

With accurate feature extraction and optimized network weights, the proposed TWO-DCNN showed the best performance in WBC classification for low-resolution and noisy data sets. It could be used as an alternative method for clinical applications.

Keywords:

• White blood cell (WBC)
• classification
• machine learning
• deep learning
• transfer learning

Acknowledgements

The authors would like to thank Shanghai Beiang Medical Technology Co., Ltd, Shanghai, China for providing with undisclosed data set. The authors also thank Prof. Gang Huang for the valuable suggestions for the revision of our paper.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Author contributions

Xufeng Yao and Kai Sun designed the study and wrote the paper. Xufeng Yao and Kai Sun managed the literature searches and analyses. Xixi Bu, Congyi Zhao and Yu Jin revised the figures, references and even conducted a series of additional experiments for the manuscript. All authors contributed to and approved the final manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data sets generated during and analyzed during the current study are available by E-mail.

Additional information

Funding

This study was funded by grants of National Natural Science Foundation of China [Nos: 61971275 and 81830052].