Bioengineering / Biyomühendislik
Permanent URI for this collectionhttps://hdl.handle.net/11147/4529
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Conference Object Deep Convolutional Neural Networks for Viability Analysis Directly From Cell Holograms Captured Using Lensless Holographic Microscopy(The Chemical and Biological Microsystems Society (CBMS), 2019) Delikoyun, Kerem; Çine, Ersin; Anıl İnevi, Müge; Özçivici, Engin; Özuysal, Mustafa; Tekin, Hüseyin CumhurCell 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.Conference Object Citation - Scopus: 2Magnetic Levitation-Based Protein Detection Using Lensless Digital Inline Holographic Microscopy(The Chemical and Biological Microsystems Society (CBMS), 2019) Yaman, Sena; Delikoyun, Kerem; Tekin, Hüseyin CumhurWe present a portable protein detection platform based on magnetic levitation principle integrated with a lensless imaging system. In the platform, polymer microspheres are used to capture selectively target proteins and magnetic nanoparticle labels. The imaging system monitors the levitation height change of polymer microspheres with respect to the presence of target protein on their surfaces. This system enables the detection of target proteins down to ng/mL levels in a short time. © 2019 CBMS-0001.