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https://hdl.handle.net/11147/9906
Title: | Deep Convolutional Neural Networks for Viability Analysis Directly From Cell Holograms Captured Using Lensless Holographic Microscopy |
Authors: | Delikoyun, Kerem Çine, Ersin Anıl İnevi, Müge Özçivici, Engin Özuysal, Mustafa Tekin, Hüseyin Cumhur |
Keywords: | Cell viability analysis Deep convolutional neural network Lensless holographic microscopy |
Publisher: | The Chemical and Biological Microsystems Society (CBMS) |
Abstract: | Cell viability analysis is one of the most widely used protocols in the fields of biomedical sciences. Traditional methods are prone to human error and require high-cost and bulky instrumentations. Lensless digital inline holographic microscopy (LDIHM) offers low-cost and high resolution imaging. However, recorded holograms should be digitally reconstructed to obtain real images, which requires intense computational work. We introduce a deep transfer learning-based cell viability classification method that directly processes the hologram without reconstruction. This new model is only trained once and viability of each cell can be predicted from its hologram. © 2019 CBMS-0001. |
Description: | Chemical and Biological Microsystems Society (CBMS) |
URI: | https://hdl.handle.net/11147/9906 |
ISBN: | 978-173341900-0 |
Appears in Collections: | Bioengineering / Biyomühendislik Computer Engineering / Bilgisayar Mühendisliği Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection |
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