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Open AccessProceedings

Artificial Neural Networks for Automated Cell Quantification in Lensless LED Imaging Systems

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Institute of Semiconductor Technology (IHT) and Laboratory for Emerging Nanometrology (LENA), Technische Universität Braunschweig, 38106 Braunschweig, Germany
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Faculty of Information Technology, Universitas Tarumanagara, Jakarta 11440, Indonesia
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Institute for Biochemistry, Biotechnology and Bioinformatics, Technische Universität Braunschweig, 38106 Braunschweig, Germany
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Department of Electrical Engineering and Information Technology (DTETI), Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
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MIND-IN2UB, Department of Electronic and Biomedical Engineering, University of Barcelona, 08028 Barcelona, Spain
*
Authors to whom correspondence should be addressed.
Presented at the Eurosensors 2018 Conference, Graz, Austria, 9–12 September 2018.
Proceedings 2018, 2(13), 989; https://doi.org/10.3390/proceedings2130989
Published: 29 November 2018
(This article belongs to the Proceedings of EUROSENSORS 2018)
Cell registration by artificial neural networks (ANNs) in combination with principal component analysis (PCA) has been demonstrated for cell images acquired by light emitting diode (LED)-based compact holographic microscopy. In this approach, principal component analysis was used to find the feature values from cells and background, which would be subsequently employed as neural inputs into the artificial neural networks. Image datasets were acquired from multiple cell cultures using a lensless microscope, where the reference data was generated by a manually analyzed recording. To evaluate the developed automatic cell counter, the trained system was assessed on different data sets to detect immortalized mouse astrocytes, exhibiting a detection accuracy of ~81% compared with manual analysis. The results show that the feature values from principal component analysis and feature learning by artificial neural networks are able to provide an automatic approach on the cell detection and registration in lensless holographic imaging.
Keywords: artificial neural networks; cell counting; lensless holographic microscopy; principal component analysis artificial neural networks; cell counting; lensless holographic microscopy; principal component analysis
MDPI and ACS Style

Dharmawan, A.B.; Scholz, G.; Mariana, S.; Hörmann, P.; Ardiyanto, I.; Wibirama, S.; Hartmann, J.; Prades, J.D.; Hiller, K.; Waag, A.; Wasisto, H.S. Artificial Neural Networks for Automated Cell Quantification in Lensless LED Imaging Systems. Proceedings 2018, 2, 989.

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