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Benchmarking Classification Models for Cell Viability on Novel Cancer Image Datasets

[ Vol. 14 , Issue. 2 ]


Akın Özkan*, Sultan Belgin İşgör, Gökhan Şengül and Yasemin Gülgün İşgör   Pages 108 - 114 ( 7 )


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 analysis.

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 “” web address publically to academic studies.


Cell viability, pattern classification, computer vision, hemocytometer, cancer cells, HL60, K562.


Department of Electrical & Electronics Engineering, Faculty of Engineering, Atilim University, Ankara, Department of Chemical Engineering and Applied Chemistry, Faculty of Engineering, Atilim University, Ankara, Department of Computer Engineering, Faculty of Engineering, Atilim University, Ankara, Medical Laboratory Techniques, Vocational School of Health, Ankara University, Ankara

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