ArticleSensors (Basel, Switzerland)2016
Machine Learning Based Single-Frame Super-Resolution Processing for Lensless Blood Cell Counting.
Article in Sensors (Basel, Switzerland), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- Advancing Platelet Research Through Live-Cell Imaging: Challenges, Techniques, and Insights.Sensors (Basel, Switzerland) · 2025Review
- Advancing Laboratory Medicine Practice With Machine Learning: Swift yet Exact.Annals of laboratory medicine · 2025Review
- Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions.ACS sensors · 2024Review
- Optical Image Sensors for Smart Analytical Chemiluminescence Biosensors.Bioengineering (Basel, Switzerland) · 2024Review
- Microfluidics on lensless, semiconductor optical image sensors: challenges and opportunities for democratization of biosensing at the micro-and nano-scale.Nanophotonics (Berlin, Germany) · 2023Review
- Bio-inspired microfluidics: A review.Biomicrofluidics · 2023Review
- YOLOv5-FPN: A Robust Framework for Multi-Sized Cell Counting in Fluorescence Images.Diagnostics (Basel, Switzerland) · 2023Article
- Computer vision meets microfluidics: a label-free method for high-throughput cell analysis.Microsystems & nanoengineering · 2023Review
- Integration of reinforcement learning to realize functional variability of microfluidic systems.Biomicrofluidics · 2022Article
- Deep Learning-Enabled Technologies for Bioimage Analysis.Micromachines · 2022Review
- An Overview of Organs-on-Chips Based on Deep Learning.Research (Washington, D.C.) · 2022Review
- Intelligent acoustofluidics enabled mini-bioreactors for human brain organoids.Lab on a chip · 2021Article
- Application of leukocyte esterase strip test in the screening of periprosthetic joint infections and prospects of high-precision strips.Arthroplasty (London, England) · 2020Review
- Capillary-Driven Flow Microfluidics Combined with Smartphone Detection: An Emerging Tool for Point-of-Care Diagnostics.Diagnostics (Basel, Switzerland) · 2020Review
- The Use of Motion Analysis as Particle Biomarkers in Lensless Optofluidic Projection Imaging for Point of Care Urine Analysis.Scientific reports · 2019Article
- Developments in Transduction, Connectivity and AI/Machine Learning for Point-of-Care Testing.Sensors (Basel, Switzerland) · 2019Article
- High-Precision Lens-Less Flow Cytometer on a Chip.Micromachines · 2018Article
- On-Chip Cell Staining and Counting Platform for the Rapid Detection of Blood Cells in Cerebrospinal Fluid.Sensors (Basel, Switzerland) · 2018Article
- Recent Advancements towards Full-System Microfluidics.Sensors (Basel, Switzerland) · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.