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Human-level blood cell counting on lens-free shadow images exploiting deep neural networks

Title
Human-level blood cell counting on lens-free shadow images exploiting deep neural networks
Author
이지영
Issue Date
2018-09
Publisher
ROYAL SOC CHEMISTRY
Citation
ANALYST, v. 143, No. 22, Page. 5380-5387
Abstract
In 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. © 2018 The Royal Society of Chemistry.
URI
https://pubs.rsc.org/en/content/articlehtml/2018/an/c8an01056khttps://repository.hanyang.ac.kr/handle/20.500.11754/105706
ISSN
0003-2654
DOI
10.1039/c8an01056k
Appears in Collections:
COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > ROBOT ENGINEERING(로봇공학과) > Articles
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