End-to-end learning via a convolutional neural network for cancer cell line classification

• Published in: Journal of industry-university collaboration Vol. 1; no. 1; pp. 17 - 23
• Main Authors: Akogo, Darlington A, Palmer, Xavier-Lewis
• Format: Journal Article
• Language: English
• Published: Bingley Emerald Publishing Limited 01-01-2019; Emerald Group Publishing Limited; Emerald Publishing
• Subjects: Accuracy; Artificial intelligence; Automation; Breast cancer; cancer cell line classification; Classification; convolutional neural network; Datasets; Deep learning; end-to-end learning; Entropy; Fashion models; Machine learning; Microscopy; Neural networks; Optimization algorithms; Support vector machines; Cancer cell line classification; End-to-end learning; Convolutional neural network
• ISSN: 2631-357X; 2631-357X
• DOI: 10.1108/JIUC-02-2019-002

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.