scholarly journals Using neural networks for high-speed blood cell classification in a holographic-microscopy flow-cytometry system

2015 ◽  
Author(s):  
B. Schneider ◽  
G. Vanmeerbeeck ◽  
R. Stahl ◽  
L. Lagae ◽  
P. Bienstman
Keyword(s):  
Neural Networks ◽  
Flow Cytometry ◽  
Blood Cell ◽  
High Speed ◽  
Cell Classification ◽  
Holographic Microscopy

scholarly journals Red blood cell classification in lensless single random phase encoding using convolutional neural networks

2020 ◽  
Vol 28 (22) ◽  
pp. 33504 ◽  
Author(s):  
Timothy O’Connor ◽  
Christopher Hawxhurst ◽  
Leslie M. Shor ◽  
Bahram Javidi
Keyword(s):  
Neural Networks ◽  
Blood Cell ◽  
Convolutional Neural Networks ◽  
Red Blood Cell ◽  
Random Phase ◽  
Cell Classification ◽  
Phase Encoding ◽  
Random Phase Encoding

scholarly journals Blood Cell Classification Using the Hough Transform and Convolutional Neural Networks

2018 ◽  
pp. 669-678 ◽  
Author(s):  
Miguel A. Molina-Cabello ◽  
Ezequiel López-Rubio ◽  
Rafael M. Luque-Baena ◽  
María Jesús Rodríguez-Espinosa ◽  
Karl Thurnhofer-Hemsi
Keyword(s):  
Neural Networks ◽  
Blood Cell ◽  
Convolutional Neural Networks ◽  
Hough Transform ◽  
Cell Classification

scholarly journals BCNet: A Novel Network for Blood Cell Classification

2022 ◽  
Vol 9 ◽  
Author(s):  
Ziquan Zhu ◽  
Siyuan Lu ◽  
Shui-Hua Wang ◽  
Juan Manuel Górriz ◽  
Yu-Dong Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Blood Cell ◽  
State Of The Art ◽  
Neural Model ◽  
Convolution Neural Network ◽  
Majority Voting ◽  
Cell Classification ◽  
Art Methods ◽  
Deep Convolution Neural Network

Aims: Most blood diseases, such as chronic anemia, leukemia (commonly known as blood cancer), and hematopoietic dysfunction, are caused by environmental pollution, substandard decoration materials, radiation exposure, and long-term use certain drugs. Thus, it is imperative to classify the blood cell images. Most cell classification is based on the manual feature, machine learning classifier or the deep convolution network neural model. However, manual feature extraction is a very tedious process, and the results are usually unsatisfactory. On the other hand, the deep convolution neural network is usually composed of massive layers, and each layer has many parameters. Therefore, each deep convolution neural network needs a lot of time to get the results. Another problem is that medical data sets are relatively small, which may lead to overfitting problems.Methods: To address these problems, we propose seven models for the automatic classification of blood cells: BCARENet, BCR5RENet, BCMV2RENet, BCRRNet, BCRENet, BCRSNet, and BCNet. The BCNet model is the best model among the seven proposed models. The backbone model in our method is selected as the ResNet-18, which is pre-trained on the ImageNet set. To improve the performance of the proposed model, we replace the last four layers of the trained transferred ResNet-18 model with the three randomized neural networks (RNNs), which are RVFL, ELM, and SNN. The final outputs of our BCNet are generated by the ensemble of the predictions from the three randomized neural networks by the majority voting. We use four multi-classification indexes for the evaluation of our model.Results: The accuracy, average precision, average F1-score, and average recall are 96.78, 97.07, 96.78, and 96.77%, respectively.Conclusion: We offer the comparison of our model with state-of-the-art methods. The results of the proposed BCNet model are much better than other state-of-the-art methods.

scholarly journals Application of neural networks to flow cytometry data analysis and real-time cell classification

Cytometry ◽  
1996 ◽  
Vol 23 (4) ◽  
pp. 290-302 ◽  
Author(s):  
Donald S. Frankel ◽  
Sheila L. Frankel ◽  
Brian J. Binder ◽  
Robert F. Vogt
Keyword(s):  
Neural Networks ◽  
Flow Cytometry ◽  
Data Analysis ◽  
Real Time ◽  
Cell Classification ◽  
Flow Cytometry Data

scholarly journals NanoLEDs for energy-efficient and gigahertz-speed spike-based sub-λ neuromorphic nanophotonic computing

Nanophotonics ◽  
2020 ◽  
Vol 9 (13) ◽  
pp. 4149-4162 ◽  
Author(s):  
Bruno Romeira ◽  
José M. L. Figueiredo ◽  
Julien Javaloyes
Keyword(s):  
Neural Networks ◽  
Energy Efficient ◽  
High Speed ◽  
Dynamic Properties ◽  
Optical Computing ◽  
Differential Conductance ◽  
Rate Equations ◽  
Dynamical Equations ◽  
Electrical Modulation ◽  
High Bandwidth

AbstractEvent-activated biological-inspired subwavelength (sub-λ) photonic neural networks are of key importance for future energy-efficient and high-bandwidth artificial intelligence systems. However, a miniaturized light-emitting nanosource for spike-based operation of interest for neuromorphic optical computing is still lacking. In this work, we propose and theoretically analyze a novel nanoscale nanophotonic neuron circuit. It is formed by a quantum resonant tunneling (QRT) nanostructure monolithic integrated into a sub-λ metal-cavity nanolight-emitting diode (nanoLED). The resulting optical nanosource displays a negative differential conductance which controls the all-or-nothing optical spiking response of the nanoLED. Here we demonstrate efficient activation of the spiking response via high-speed nonlinear electrical modulation of the nanoLED. A model that combines the dynamical equations of the circuit which considers the nonlinear voltage-controlled current characteristic, and rate equations that takes into account the Purcell enhancement of the spontaneous emission, is used to provide a theoretical framework to investigate the optical spiking dynamic properties of the neuromorphic nanoLED. We show inhibitory- and excitatory-like optical spikes at multi-gigahertz speeds can be achieved upon receiving exceptionally low (sub-10 mV) synaptic-like electrical activation signals, lower than biological voltages of 100 mV, and with remarkably low energy consumption, in the range of 10–100 fJ per emitted spike. Importantly, the energy per spike is roughly constant and almost independent of the incoming modulating frequency signal, which is markedly different from conventional current modulation schemes. This method of spike generation in neuromorphic nanoLED devices paves the way for sub-λ incoherent neural elements for fast and efficient asynchronous neural computation in photonic spiking neural networks.

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