Abstract
In medicine, white blood cells (WBCs) play an important role in the human immune system. The different types of WBC abnormalities are related to different diseases so that the total number and classification of WBCs are critical for clinical diagnosis and therapy. However, the traditional method of white blood cell classification is to segment the cells, extract features, and then classify them. Such method depends on the good segmentation, and the accuracy is not high. Moreover, the insufficient data or unbalanced samples can cause the low classification accuracy of model by using deep learning in medical diagnosis. To solve these problems, this paper proposes a new blood cell image classification framework which is based on a deep convolutional generative adversarial network (DC-GAN) and a residual neural network (ResNet). In particular, we introduce a new loss function which is improved the discriminative power of the deeply learned features. The experiments show that our model has a good performance on the classification of WBC images, and the accuracy reaches 91.7%.
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Acknowledgments
I would like to thank the College of Computer Science and Technology of Nanjing Tech University for providing us with a server for running the code. I would like to thank Dr. Wenjia Liu from Nanjing Drum Tower Hospital for her guidance and help with the medical knowledge of this article. Thanks to other authors who contributed to this article.
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Ma, L., Shuai, R., Ran, X. et al. Combining DC-GAN with ResNet for blood cell image classification. Med Biol Eng Comput 58, 1251–1264 (2020). https://doi.org/10.1007/s11517-020-02163-3
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DOI: https://doi.org/10.1007/s11517-020-02163-3