Document Type
Article
Publication Date
2019
DOI
10.1108/JIUC-02-2019-002
Publication Title
Journal of Industry-University Collaboration
Volume
1
Issue
1
Pages
17-23
Abstract
Purpose: Computer vision for automated analysis of cells and tissues usually include extracting features from images before analyzing such features via various machine learning and machine vision algorithms. The purpose of this work is to explore and demonstrate the ability of a Convolutional Neural Network (CNN) to classify cells pictured via brightfield microscopy without the need of any feature extraction, using a minimum of images, improving work-flows that involve cancer cell identification.
Design/methodology/approach: The methodology involved a quantitative measure of the performance of a Convolutional Neural Network in distinguishing between two cancer lines. In their approach, they trained, validated and tested their 6-layer CNN on 1,241 images of MDA-MB-468 and MCF7 breast cancer cell line in an end-to-end fashion, allowing the system to distinguish between the two different cancer cell types.
Findings: They obtained a 99% accuracy, providing a foundation for more comprehensive systems.
Originality/value: Value can be found in that systems based on this design can be used to assist cell identification in a variety of contexts, whereas a practical implication can be found that these systems can be deployed to assist biomedical workflows quickly and at low cost. In conclusion, this system demonstrates the potentials of end-to-end learning systems for faster and more accurate automated cell analysis.
Original Publication Citation
Akogo, D. A., & Palmer, X.-L. (2018). End-to-end learning via a convolutional neural network for cancer cell line classification. Journal of Industry-University Collaboration, 1(1), 17-23. https://doi.org/10.1108/JIUC-02-2019-002
Repository Citation
Akogo, Darlington A. and Palmer, Xavier-Lewis, "End-to-End Learning Via a Convolutional Neural Network for Cancer Cell Line Classification" (2019). Electrical & Computer Engineering Faculty Publications. 313.
https://digitalcommons.odu.edu/ece_fac_pubs/313
ORCID
0000-0002-1289-5302 (Palmer)
Included in
Artificial Intelligence and Robotics Commons, Bioimaging and Biomedical Optics Commons, Cancer Biology Commons
Comments
Copyright © 2019, Darlington A. Akogo and Xavier-Lewis Palmer
This article is published under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors.