Deep-Learning Based Label-Free Classification of Activated and Inactivated Neutrophils for Rapid Immune State Monitoring

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 2; p. 512
• Main Authors: Huang, Xiwei, Jeon, Hyungkook, Liu, Jixuan, Yao, Jiangfan, Wei, Maoyu, Han, Wentao, Chen, Jin, Sun, Lingling, Han, Jongyoon
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.01.2021; MDPI
• Subjects: Accuracy; Automation; Blood; Blood cell count; Classification; Datasets; Deep Learning; Feature selection; Fluorescence; Humans; Immune system; label-free; Leukocyte Count; Machine learning; Morphology; Neural Networks, Computer; neutrophil activation; Neutrophils; Physiological aspects; Physiology; point-of-care; Signal transduction; Support vector machines; transfer learning; Variance analysis; white blood cell classification; Taiwan; neutrophil activation; white blood cell classification; deep learning; transfer learning; label-free; point-of-care
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21020512

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.