Abstract
The detection of cell viability or the detection of the percentage of live and dead cells in a sample of cells is an important parameter. At present, the common methods for cell viability determination mainly rely on the responses to cell dyes. However, the additional need for cell staining will consequently cause time-consuming and laborious efforts. Furthermore, the determination of cell viability by cell staining is invasive and may damage the internal structure of cells. In this work, we proposed a label-free method to classify live and dead colonic adenocarcinoma cells by 2D light scattering combined with 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 (92.16%), specificity (94.23%), and accuracy (93.2%). The results show that the combination of 2D light scattering images and deep neural network may provide a new label-free method for cellular analysis.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
