박태준
2019-05-23T01:50:38Z
2019-05-23T01:50:38Z
2018-11
ANALYST, v. 143, No. 22, Page. 5380-5387
0003-2654
1364-5528
https://pubs.rsc.org/en/content/articlehtml/2018/an/c8an01056k
https://repository.hanyang.ac.kr/handle/20.500.11754/105777
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.
This 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
ROYAL SOC CHEMISTRY
Human-level blood cell counting on lens-free shadow images exploiting deep neural networks
Article
22
143
10.1039/c8an01056k
5380-5387
ANALYST
Ahn, DaeHan
Lee, JiYeong
Moon, SangJun
Park, Taejoon
2018001992
E
COLLEGE OF ENGINEERING SCIENCES[E]
DEPARTMENT OF ROBOT ENGINEERING
taejoon