The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Label‐free classification of dead and live colonic adenocarcinoma cells based on 2D light scattering and deep learning analysis
Open accessAbstract
The measurement of cell viability plays an essential role in the area of cell biology. At present, the common methods for cell viability assay mainly on the responses of cells to different dyes. However, the additional steps of cell staining will consequently cause time‐consuming and laborious efforts. Furthermore, the process of cell staining is invasive and may cause internal structure damage of cells, restricting their reuse in subsequent experiments. In this work, we proposed a label‐free method to classify live and dead colonic adenocarcinoma cells by 2D light scattering combined with the deep learning algorithm. The deep convolutional network of YOLO‐v3 was used to identify and classify light scattering images of live and dead HT29 cells. This method achieved an excellent sensitivity (93.6%), specificity (94.4%), and accuracy (94%). The results showed that the combination of 2D light scattering images and deep neural network may provide a new label‐free method for cellular analysis.
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“| YOLO v3 [72] | Accuracy, Specificity, Sensitivity | Introduced a label-free method for cellular analysis using 2D light scattering and neural networks. | Oncology |”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [72]
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Ali, M. L., & Zhang, Z. (2024) · Computers · doi:10.3390/computers13120336 · Cited by 383
A Comprehensive Systematic Review of YOLO for Medical Object Detection (2018 to 2023)
Ragab, M. G., Abdulkadir, S. J., Muneer, A., Alqushaibi, A., Sumiea, E. H., Qureshi, R., Al-Selwi, S. M., & Alhussian, H. (2024) · IEEE Access · doi:10.1109/access.2024.3386826 · Cited by 306
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Self-citationA Comprehensive Systematic Review of YOLO for Medical Object Detection (2018 to 2023)Preprint
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Morphology-Based Deep Learning Approach for Predicting Osteogenic Differentiation
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Metadata
- Authors
- Li, S., Li, Y., Yao, J., Chen, B., Song, J., Xue, Q., & Yang, X.
- Affiliations
- Zhengzhou University (CN)
- First Affiliated Hospital of Zhengzhou University (CN)
- Corresponding author
- Xue, Qi
- Yang, Xiaonan
- Venue
- Cytometry Part A
- Publisher
- Wiley
- Published
- 2021-06-19
- Volume
- 99
- Issue
- 11
- Pages
- 1134-1142
- ISSN
- 1552-4922 (ISSN-L)
- 1552-4930
- OpenAlex
- W3170879873
- Cited by
- 13
- References
- 24
- Citations by year
- 2026: 3 · 2025: 1 · 2024: 4 · 2023: 3 · 2022: 2
- FWCI
- 1.02
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Cytometry Part A (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (61874099, 81802325)
- China Postdoctoral Science Foundation (2018M642796)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Digital Holography and Microscopy
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Spectroscopy Techniques in Biomedical and Chemical Research
- Keywords
- HT-29 cells, YOLOv3, deep convolutional neural networks
- MeSH terms
- Deep Learning, Adenocarcinoma, Algorithms, Humans, Staining and Labeling, Neural Networks, Computer
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- CoChat. (2026). Citation check: Label‐free classification of dead and live colonic adenocarcinoma cells based on 2D light scattering and deep learning analysis. https://app.cochat.ai/papers/10.1002/cyto.a.24475
- The paper
- Li, S., Li, Y., Yao, J., Chen, B., Song, J., Xue, Q., & Yang, X. (2021). Label‐free classification of dead and live colonic adenocarcinoma cells based on 2D light scattering and deep learning analysis. Cytometry Part A. https://doi.org/10.1002/cyto.a.24475
