Background: Dye-exclusion based cell viability analysis has been broadly used in cell
biology including anticancer drug discovery studies. Viability analysis refers to the whole decision
making process for the distinction of dead cells from live ones. Basically, cell culture samples are
dyed with a special stain called trypan blue, so that the dead cells are selectively colored to
darkish. This distinction provides critical information that may be used to expose influences of the
studied drug on considering cell culture including cancer. Examiner’s experience and tiredness
substantially affect the consistency throughout the manual observation of cell viability. The
unsteady results of cell viability may end up with biased experimental results accordingly.
Therefore, a machine learning based automated decision-making procedure is inevitably needed to
improve consistency of the cell viability analysis.
Objective: In this study, we investigate various combinations of classifiers and feature extractors
(i.e. classification models) to maximize the performance of computer vision-based viability
Method: The classification models are tested on novel hemocytometer image datasets which
contain two types of cancer cell images, namely, caucasian promyelocytic leukemia (HL60), and
chronic myelogenous leukemia (K562).
Results: From the experimental results, k-Nearest Neighbor (KNN) and Random Forest (RF) by
combining Local Phase Quantization (LPQ) achieve the lowest misclassification rates that are
0.031 and 0.082, respectively.
Conclusion: The experimental results show that KNN and RF with LPQ can be powerful
alternatives to the conventional manual cell viability analysis. Also, the collected datasets are
released from the “biochem.atilim.edu.tr/datasets/” web address publically to academic studies.