.png){kind=icon}
返回
Deep Learning Aided Small-Sized Portable Fluorescence Biochip Reader
.png){kind=icon}
PRE
AI
DOI：10.1109/TIM.2022.3165788.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We present here the development of a novel fluorescence-based portable hiochip reader system, having a novel machine learning method for fluorescence measurement of multianalytes. It is suitable for reading DNA/protein biochips in basic research and clinical diagnosis. We aim to develop a point-of-care diagnostic device in the healthcare sector to accelerate biomedical research at an affordable cost. Innovation is involved in the design of each module of the system that is compact, detachable, and reconfigurable. The system can be used with multiple fluorescent dyes depending on specific requirements. It provides high-sensitivity and high-resolution fluorescent images with lesser scan time compared to currently available state-of-the-art systems. The system is autoaligned/autofocus with a standard microscopic glass slide and custom-developed membrane-based biochip. A new deep learning network Bio-U-Net has been developed for real-time detection and quantification of microarrays. On the real test images, it achieved the precision of 0.9928 and the sensitivity of 0.9698 along with F1-score of 0.9812. The system and deep learning network have been extensively tested and these together provide a low-cast option while maintaining the required accuracy.
Keyword:
Biochip
convolutional neural network (CNN)
fluorescence detection
microarrays
scanner
U-Net
期刊
IF:
5.9
论文数:
2.0W
被引数:
5.8W
机构
引用论文
Electrochemically assisted micro localized grafting of aptamers in a microchannel engraved in fluorinated thermoplastic polymer Dyneon THV.png)
RSC Advances
RSC AdvancesIF0
PREAI
分享收藏分享收藏
A Novel Handheld High-Throughput Device for Rapid Detection of Phytoplankton in Ship's Ballast Water
PREAI
分享收藏The Influence of Appropriate Staffing and Healthy Work Environments on Patient and Nurse Outcomes
PREAI
分享收藏
查看更多内容