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Fractal dimension-based viability analysis of cancer cell lines in lens-free holographic microscopy via machine learning

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Abstract

Cell studies play an important role in the basis of studies on cancer diagnosis and treatment. Reliable viability assays on cancer cell studies are essential for the development of effective drugs. Lens-free digital in-line holographic microscopy has become a powerful tool in the characterization and viability analysis of microparticles such as cancer cells due to its advantages such as high efficiency, low cost, and flexibility to integrate with other components. This study is designed to perform viability tests using fractal dimensions of alive and dead cancer cells based on digital holographic microscopy and machine learning. In the in-line holography configuration, a microscopy assembly consisting of inexpensive components was built using an LED source, and the images were reconstructed using computational methods. The standard US Air Force Resolution Target was used to evaluate the capability of our imaging setup then holograms of stained cancer cells were recorded. To characterize individual cells, 19 different rotational invariant fractal dimension values were extracted from the images as features. An artificial neural network technique was employed for the classification of fractal features extracted from cells. The artificial neural network was compared with four other machine learning techniques through five different classification performance measures. The empirical results indicated that artificial neural networks performed better than compared classification techniques with accuracies of 99.65%. The method proposed in this paper provides a new method for the study of cell viability which has the advantages of high accuracy and potential for laboratory application.

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Acknowledgements

This work was supported by Sakarya University of Applied Science Scientific Research Projects Coordination Unit (SUBU BAPK, Project Number: 2020-01-01-011). The author, Muhammed Ali PALA, is grateful to The Scientific and Technological Research Council of Turkey for granting a scholarship (TUBITAK, 2211-C) for him Ph.D. studies.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Faculty of Technology, Sakarya University of Applied Sciences, Sakarya, 54050, Turkey

    Muhammed Ali Pala, Murat Erhan Çimen, Mustafa Zahid Yıldız & Ali Fuat Boz

  2. Department of Computer Engineering, Faculty of Engineering, Hitit University, Çorum, 19030, Turkey

    Akif Akgül

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  1. Muhammed Ali Pala
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  2. Murat Erhan Çimen
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  3. Akif Akgül
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Correspondence to Muhammed Ali Pala.

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Pala, M.A., Çimen, M.E., Akgül, A. et al. Fractal dimension-based viability analysis of cancer cell lines in lens-free holographic microscopy via machine learning. Eur. Phys. J. Spec. Top. 231, 1023–1034 (2022). https://doi.org/10.1140/epjs/s11734-021-00342-3

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