Deep-Learning Based Label-Free Classification of Activated and Inactivated Neutrophils for Rapid Immune State Monitoring
Name
sensors-21-00512-v2.pdf
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6.86 MB
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Checksum (MD5)
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Author(s)
Huang, Xiwei
Jeon, Hyungkook
Liu, Jixuan
Yao, Jiangfan
Wei, Maoyu
Han, Wentao
Chen, Jin
Sun, Lingling
Han, Jongyoon
Date Issued
January 13, 2021
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Sensors 21 (2): 512 (2021)
Version
Final published version
Abstract
The differential count of white blood cells (WBCs) is one widely used approach to assess the status of a patient’s immune system. Currently, the main methods of differential WBC counting are manual counting and automatic instrument analysis with labeling preprocessing. But these two methods are complicated to operate and may interfere with the physiological states of cells. Therefore, we propose a deep learning-based method to perform label-free classification of three types of WBCs based on their morphologies to judge the activated or inactivated neutrophils. Over 90% accuracy was finally achieved by a pre-trained fine-tuning Resnet-50 network. This deep learning-based method for label-free WBC classification can tackle the problem of complex instrumental operation and interference of fluorescent labeling to the physiological states of the cells, which is promising for future point-of-care applications.
MIT Department
Massachusetts Institute of Technology. Research Laboratory of Electronics
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Biological Engineering
Terms of Use
Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.3390/s21020512
