Convolutional neural networks for particle shape classification using light-scattering patterns

2020 ◽  
Vol 245 ◽  
pp. 106901 ◽  
Author(s):  
Chizhu Ding
Keyword(s):  
Neural Networks ◽  
Light Scattering ◽  
Convolutional Neural Networks ◽  
Particle Shape ◽  
Shape Classification

Theoretical studies on particle shape classification based on simultaneous small forward angle light scattering and aerodynamic sizing

2016 ◽  
Vol 25 (3) ◽  
pp. 034201 ◽  
Author(s):  
Jin-Bi Zhang ◽  
Lei Ding ◽  
Ying-Ping Wang ◽  
Li Zhang ◽  
Jin-Lei Wu ◽  
...  
Keyword(s):  
Light Scattering ◽  
Particle Shape ◽  
Shape Classification ◽  
Theoretical Studies ◽  
Forward Angle

Particle-shape classification using light scattering: An exercise in deep learning

2019 ◽  
Vol 231 ◽  
pp. 140-156 ◽  
Author(s):  
Patricio Piedra ◽  
Aimable Kalume ◽  
Evgenij Zubko ◽  
Daniel Mackowski ◽  
Yong-Le Pan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Light Scattering ◽  
Particle Shape ◽  
Shape Classification

The application of neural networks to particle shape classification

1992 ◽  
Vol 23 ◽  
pp. 329-332 ◽  
Author(s):  
AV Bevan ◽  
S.A. Bryant ◽  
JM Clark ◽  
K Reid
Keyword(s):  
Neural Networks ◽  
Particle Shape ◽  
Shape Classification

scholarly journals 3D shape classification based on convolutional neural networks fusing multi-view information

2019 ◽  
Vol 49 (4) ◽  
pp. 436-449
Author(s):  
Pengyu WANG ◽  
Panpan SHUI ◽  
Fenggen YU ◽  
Yuan GAN ◽  
Kun LIU ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Shape Classification ◽  
3D Shape

scholarly journals Towards Intraoperative Quantification of Atrial Fibrosis Using Light-Scattering Spectroscopy and Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6033
Author(s):  
Nathan J. Knighton ◽  
Brian K. Cottle ◽  
Bailey E. B. Kelson ◽  
Robert W. Hitchcock ◽  
Frank B. Sachse
Keyword(s):  
Neural Networks ◽  
Light Scattering ◽  
Convolutional Neural Networks ◽  
Volume Fraction ◽  
Biological Tissues ◽  
Atrial Fibrosis ◽  
Cardiac Tissues ◽  
Tissue Constructs ◽  
Light Scattering Spectroscopy

Light-scattering spectroscopy (LSS) is an established optical approach for characterization of biological tissues. Here, we investigated the capabilities of LSS and convolutional neural networks (CNNs) to quantitatively characterize the composition and arrangement of cardiac tissues. We assembled tissue constructs from fixed myocardium and the aortic wall with a thickness similar to that of the atrial free wall. The aortic sections represented fibrotic tissue. Depth, volume fraction, and arrangement of these fibrotic insets were varied. We gathered spectra with wavelengths from 500–1100 nm from the constructs at multiple locations relative to a light source. We used single and combinations of two spectra for training of CNNs. With independently measured spectra, we assessed the accuracy of the CNNs for the classification of tissue constructs from single spectra and combined spectra. Combined spectra, including the spectra from fibers distal from the illumination fiber, typically yielded the highest accuracy. The maximal classification accuracy of the depth detection, volume fraction, and permutated arrangements was (mean ± standard deviation (stddev)) 88.97 ± 2.49%, 76.33 ± 1.51%, and 84.25 ± 1.88%, respectively. Our studies demonstrate the reliability of quantitative characterization of tissue composition and arrangements using a combination of LSS and CNNs. The potential clinical applications of the developed approach include intraoperative quantification and mapping of atrial fibrosis, as well as the assessment of ablation lesions.

Sign in / Sign up

Export Citation Format

Share Document