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dc.contributor.author박태준-
dc.date.accessioned2019-05-23T01:50:38Z-
dc.date.available2019-05-23T01:50:38Z-
dc.date.issued2018-11-
dc.identifier.citationANALYST, v. 143, No. 22, Page. 5380-5387en_US
dc.identifier.issn0003-2654-
dc.identifier.issn1364-5528-
dc.identifier.urihttps://pubs.rsc.org/en/content/articlehtml/2018/an/c8an01056k-
dc.identifier.urihttps://repository.hanyang.ac.kr/handle/20.500.11754/105777-
dc.description.abstractIn point-of-care testing, in-line holographic microscopes paved the way for realizing portable cell counting systems at marginal cost. To maximize their accuracy, it is critically important to reliably count the number of cells even in noisy blood images overcoming various problems due to out-of-focus blurry cells and background brightness variations. However, previous studies could detect cells only on clean images while they failed to accurately distinguish blurry cells from background noises. To address this problem, we present a human-level blood cell counting system by synergistically integrating the methods of normalized cross-correlation (NCC) and a convolutional neural network (CNN). Our comprehensive performance evaluation demonstrates that the proposed system achieves the highest level of accuracy (96.7–98.4%) for any kinds of blood cells on a lens-free shadow image while others suffer from significant accuracy degradations (12.9–38.9%) when detecting blurry cells. Moreover, it outperforms others by up to 36.8% in accurately analyzing noisy blood images and is 24.0–40.8× faster, thus maximizing both accuracy and computational efficiency.en_US
dc.description.sponsorshipThis work was supported by the Institute for Information & Communications Technology Promotion (IITP) grant funded by the Ministry of Science and ICT (No. 2017-0-00373-001).en_US
dc.language.isoen_USen_US
dc.publisherROYAL SOC CHEMISTRYen_US
dc.titleHuman-level blood cell counting on lens-free shadow images exploiting deep neural networksen_US
dc.typeArticleen_US
dc.relation.no22-
dc.relation.volume143-
dc.identifier.doi10.1039/c8an01056k-
dc.relation.page5380-5387-
dc.relation.journalANALYST-
dc.contributor.googleauthorAhn, DaeHan-
dc.contributor.googleauthorLee, JiYeong-
dc.contributor.googleauthorMoon, SangJun-
dc.contributor.googleauthorPark, Taejoon-
dc.relation.code2018001992-
dc.sector.campusE-
dc.sector.daehakCOLLEGE OF ENGINEERING SCIENCES[E]-
dc.sector.departmentDEPARTMENT OF ROBOT ENGINEERING-
dc.identifier.pidtaejoon-
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COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > ROBOT ENGINEERING(로봇공학과) > Articles
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