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Sensors 2016, 16(11), 1836; doi:10.3390/s16111836

Machine Learning Based Single-Frame Super-Resolution Processing for Lensless Blood Cell Counting

1
Ministry of Education Key Lab of RF Circuits and Systems, Hangzhou Dianzi University, Hangzhou 310018, China
2
School of Microelectronics, Southeast University, Wuxi 214135, China
3
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
*
Authors to whom correspondence should be addressed.
Academic Editors: Amine Miled and Jesse Greener
Received: 25 August 2016 / Revised: 14 October 2016 / Accepted: 21 October 2016 / Published: 2 November 2016
(This article belongs to the Special Issue Microfluidics-Based Microsystem Integration Research)
View Full-Text   |   Download PDF [3738 KB, uploaded 2 November 2016]   |  

Abstract

A lensless blood cell counting system integrating microfluidic channel and a complementary metal oxide semiconductor (CMOS) image sensor is a promising technique to miniaturize the conventional optical lens based imaging system for point-of-care testing (POCT). However, such a system has limited resolution, making it imperative to improve resolution from the system-level using super-resolution (SR) processing. Yet, how to improve resolution towards better cell detection and recognition with low cost of processing resources and without degrading system throughput is still a challenge. In this article, two machine learning based single-frame SR processing types are proposed and compared for lensless blood cell counting, namely the Extreme Learning Machine based SR (ELMSR) and Convolutional Neural Network based SR (CNNSR). Moreover, lensless blood cell counting prototypes using commercial CMOS image sensors and custom designed backside-illuminated CMOS image sensors are demonstrated with ELMSR and CNNSR. When one captured low-resolution lensless cell image is input, an improved high-resolution cell image will be output. The experimental results show that the cell resolution is improved by 4×, and CNNSR has 9.5% improvement over the ELMSR on resolution enhancing performance. The cell counting results also match well with a commercial flow cytometer. Such ELMSR and CNNSR therefore have the potential for efficient resolution improvement in lensless blood cell counting systems towards POCT applications. View Full-Text
Keywords: microfluidic cytometer; super-resolution; convolutional neural network; extreme learning machine; CMOS image sensor; point-of-care testing microfluidic cytometer; super-resolution; convolutional neural network; extreme learning machine; CMOS image sensor; point-of-care testing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Huang, X.; Jiang, Y.; Liu, X.; Xu, H.; Han, Z.; Rong, H.; Yang, H.; Yan, M.; Yu, H. Machine Learning Based Single-Frame Super-Resolution Processing for Lensless Blood Cell Counting. Sensors 2016, 16, 1836.

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