Deep Convolutional Neural Networks for Viability Analysis Directly From Cell Holograms Captured Using Lensless Holographic Microscopy

Delikoyun, KeremÇine, ErsinAnıl İnevi, MügeÖzçivici, EnginÖzuysal, MustafaTekin, Hüseyin Cumhur03.01. Department of Bioengineering03.04. Department of Computer Engineering03. Faculty of Engineering01. Izmir Institute of Technology2021-01-242021-01-242019978-173341900-0https://hdl.handle.net/11147/9906Chemical and Biological Microsystems Society (CBMS)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.eninfo:eu-repo/semantics/closedAccessCell viability analysisDeep convolutional neural networkLensless holographic microscopyDeep Convolutional Neural Networks for Viability Analysis Directly From Cell Holograms Captured Using Lensless Holographic MicroscopyConference Object2-s2.0-85094963341