Deep-Learning Based Label-Free Classification of Activated and Inactivated Neutrophils for Rapid Immune State Monitoring
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 comp...
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| Format: | Article |
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MDPI AG
2021-01-01
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| Online Access: | https://www.mdpi.com/1424-8220/21/2/512 |
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| author | Xiwei Huang Hyungkook Jeon Jixuan Liu Jiangfan Yao Maoyu Wei Wentao Han Jin Chen Lingling Sun Jongyoon Han |
| author_facet | Xiwei Huang Hyungkook Jeon Jixuan Liu Jiangfan Yao Maoyu Wei Wentao Han Jin Chen Lingling Sun Jongyoon Han |
| author_sort | Xiwei Huang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T04:59:14Z |
| format | Article |
| id | doaj.art-8e2d71c4eec54560893cdab3940698c9 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T04:59:14Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8e2d71c4eec54560893cdab3940698c92023-12-03T13:02:16ZengMDPI AGSensors1424-82202021-01-0121251210.3390/s21020512Deep-Learning Based Label-Free Classification of Activated and Inactivated Neutrophils for Rapid Immune State MonitoringXiwei Huang0Hyungkook Jeon1Jixuan Liu2Jiangfan Yao3Maoyu Wei4Wentao Han5Jin Chen6Lingling Sun7Jongyoon Han8Key Laboratory of RF Circuits and Systems, Ministry of Education, Hangzhou Dianzi University, Hangzhou 310018, ChinaResearch Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139, USAKey Laboratory of RF Circuits and Systems, Ministry of Education, Hangzhou Dianzi University, Hangzhou 310018, ChinaKey Laboratory of RF Circuits and Systems, Ministry of Education, Hangzhou Dianzi University, Hangzhou 310018, ChinaKey Laboratory of RF Circuits and Systems, Ministry of Education, Hangzhou Dianzi University, Hangzhou 310018, ChinaKey Laboratory of RF Circuits and Systems, Ministry of Education, Hangzhou Dianzi University, Hangzhou 310018, ChinaKey Laboratory of RF Circuits and Systems, Ministry of Education, Hangzhou Dianzi University, Hangzhou 310018, ChinaKey Laboratory of RF Circuits and Systems, Ministry of Education, Hangzhou Dianzi University, Hangzhou 310018, ChinaResearch Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139, USAThe 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.https://www.mdpi.com/1424-8220/21/2/512label-freewhite blood cell classificationdeep learningtransfer learningneutrophil activationpoint-of-care |
| spellingShingle | Xiwei Huang Hyungkook Jeon Jixuan Liu Jiangfan Yao Maoyu Wei Wentao Han Jin Chen Lingling Sun Jongyoon Han Deep-Learning Based Label-Free Classification of Activated and Inactivated Neutrophils for Rapid Immune State Monitoring Sensors label-free white blood cell classification deep learning transfer learning neutrophil activation point-of-care |
| title | Deep-Learning Based Label-Free Classification of Activated and Inactivated Neutrophils for Rapid Immune State Monitoring |
| title_full | Deep-Learning Based Label-Free Classification of Activated and Inactivated Neutrophils for Rapid Immune State Monitoring |
| title_fullStr | Deep-Learning Based Label-Free Classification of Activated and Inactivated Neutrophils for Rapid Immune State Monitoring |
| title_full_unstemmed | Deep-Learning Based Label-Free Classification of Activated and Inactivated Neutrophils for Rapid Immune State Monitoring |
| title_short | Deep-Learning Based Label-Free Classification of Activated and Inactivated Neutrophils for Rapid Immune State Monitoring |
| title_sort | deep learning based label free classification of activated and inactivated neutrophils for rapid immune state monitoring |
| topic | label-free white blood cell classification deep learning transfer learning neutrophil activation point-of-care |
| url | https://www.mdpi.com/1424-8220/21/2/512 |
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