Machine learning based single-frame super-resolution processing for lensless blood cell counting

Sensors (Switzerland)

Volumn 16, Issue 11, 2016, Pages

  Huang, Xiwei ^a,b   Jiang, Yu ^c   Liu, Xu ^c   Xu, Hang ^c   Han, Zhi ^b   Rong, Hailong ^b   Yang, Haiping ^b   Yan, Mei ^b   Yu, Hao ^c  

^a HANGZHOU DIANZI UNIVERSITY   (China)

^b SOUTHEAST UNIVERSITY   (China)

^c NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

CMOS image sensor; Convolutional neural network; Extreme learning machine; Microfluidic cytometer; Point of care testing; Super resolution

Indexed keywords

BLOOD; CMOS INTEGRATED CIRCUITS; CONVOLUTION; CYTOLOGY; DIGITAL CAMERAS; IMAGE ENHANCEMENT; IMAGE SENSORS; INTEGRATION TESTING; KNOWLEDGE ACQUISITION; LEARNING SYSTEMS; LENSES; METAL TESTING; METALS; MICROFLUIDICS; MOS DEVICES; NEURAL NETWORKS; OPTICAL RESOLVING POWER; OXIDE SEMICONDUCTORS; PIXELS;

CMOS IMAGE SENSOR; CONVOLUTIONAL NEURAL NETWORK; EXTREME LEARNING MACHINE; POINT-OF-CARE TESTING; SUPER RESOLUTION;

CELLS;

BLOOD CELL COUNT; FLOW CYTOMETRY; HUMAN; MACHINE LEARNING; MICROFLUIDIC ANALYSIS; PROCEDURES; SEMICONDUCTOR;

BLOOD CELL COUNT; FLOW CYTOMETRY; HUMANS; MACHINE LEARNING; MICROFLUIDIC ANALYTICAL TECHNIQUES; SEMICONDUCTORS;

EID: 84994756552     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s16111836     Document Type: Article

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