Classification of crystal structures using electron diffraction patterns with a deep convolutional neural network

The off-the-shelf deep convolutional neural network architecture, ResNet, could classify the space group of materials with cubic crystal structures with the prediction accuracy of 92.607%, using the selected area electron diffraction patterns.

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Classification of Human Driving Behaviour Images Using Convolutional Neural Network Architecture

Lecture Notes in Computer Science - Hybrid Artificial Intelligent Systems ◽

10.1007/978-3-030-29859-3_23 ◽

2019 ◽

pp. 264-274

Author(s):

Emine Cengil ◽

Ahmet Cinar

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Architecture ◽

Neural Network Architecture ◽

Driving Behaviour

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Deep Convolutional Neural Network Architecture With Reconfigurable Computation Patterns

IEEE Transactions on Very Large Scale Integration (VLSI) Systems ◽

10.1109/tvlsi.2017.2688340 ◽

2017 ◽

Vol 25 (8) ◽

pp. 2220-2233 ◽

Cited By ~ 88

Author(s):

Fengbin Tu ◽

Shouyi Yin ◽

Peng Ouyang ◽

Shibin Tang ◽

Leibo Liu ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Architecture ◽

Deep Convolutional Neural Network ◽

Neural Network Architecture

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Low resolution face recognition using a two-branch deep convolutional neural network architecture

Expert Systems with Applications ◽

10.1016/j.eswa.2019.112854 ◽

2020 ◽

Vol 139 ◽

pp. 112854 ◽

Cited By ~ 14

Author(s):

Erfan Zangeneh ◽

Mohammad Rahmati ◽

Yalda Mohsenzadeh

Keyword(s):

Neural Network ◽

Face Recognition ◽

Convolutional Neural Network ◽

Network Architecture ◽

Deep Convolutional Neural Network ◽

Low Resolution ◽

Neural Network Architecture

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Automatic segmentation of whole-slide H&E stained breast histopathology images using a deep convolutional neural network architecture

Expert Systems with Applications ◽

10.1016/j.eswa.2020.113387 ◽

2020 ◽

Vol 151 ◽

pp. 113387 ◽

Cited By ~ 2

Author(s):

Blanca Maria Priego-Torres ◽

Daniel Sanchez-Morillo ◽

Miguel Angel Fernandez-Granero ◽

Marcial Garcia-Rojo

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Architecture ◽

Automatic Segmentation ◽

Deep Convolutional Neural Network ◽

Neural Network Architecture ◽

Histopathology Images

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A Novel Convolutional Neural Network Architecture for SAR Target Recognition

Journal of Sensors ◽

10.1155/2019/1246548 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 5

Author(s):

Yinjie Xie ◽

Wenxin Dai ◽

Zhenxin Hu ◽

Yijing Liu ◽

Chuan Li ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Architecture ◽

Target Recognition ◽

Automatic Target Recognition ◽

Neural Network Architecture ◽

Data Set ◽

Stationary Target ◽

Optical Images

Among many improved convolutional neural network (CNN) architectures in the optical image classification, only a few were applied in synthetic aperture radar (SAR) automatic target recognition (ATR). One main reason is that direct transfer of these advanced architectures for the optical images to the SAR images easily yields overfitting due to its limited data set and less features relative to the optical images. Thus, based on the characteristics of the SAR image, we proposed a novel deep convolutional neural network architecture named umbrella. Its framework consists of two alternate CNN-layer blocks. One block is a fusion of six 3-layer paths, which is used to extract diverse level features from different convolution layers. The other block is composed of convolution layers and pooling layers are mainly utilized to reduce dimensions and extract hierarchical feature information. The combination of the two blocks could extract rich features from different spatial scale and simultaneously alleviate overfitting. The performance of the umbrella model was validated by the Moving and Stationary Target Acquisition and Recognition (MSTAR) benchmark data set. This architecture could achieve higher than 99% accuracy for the classification of 10-class targets and higher than 96% accuracy for the classification of 8 variants of the T72 tank, even in the case of diverse positions located by targets. The accuracy of our umbrella is superior to the current networks applied in the classification of MSTAR. The result shows that the umbrella architecture possesses a very robust generalization capability and will be potential for SAR-ART.

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COVIDNet-CT: Detection of COVID-19 from Chest CT Images using a Tailored Deep Convolutional Neural Network Architecture

Journal of Computational Vision and Imaging Systems ◽

10.15353/jcvis.v6i1.3547 ◽

2021 ◽

Vol 6 (1) ◽

pp. 1-3

Author(s):

Hayden Gunraj ◽

Linda Wang ◽

Alexander Wong

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Architecture ◽

Screening Method ◽

Ct Images ◽

Chest Ct ◽

Deep Convolutional Neural Network ◽

Screening Tools ◽

Neural Network Architecture ◽

Tremendous Impact

The COVID-19 pandemic continues to have a tremendous impact on patients and healthcare systems around the world. To combat this disease, there is a need for effective screening tools to identify patients infected with COVID-19, and to this end CT imaging has been proposed as a key screening method to complement RT-PCR testing. Early studies have reported abnormalities in chest CT images which are characteristic of COVID-19 infection, but these abnormalities may be difficult to distinguish from abnormalities caused by other lung conditions. Motivated by this, we introduce COVIDNet-CT, a deep convolutional neural network architecture tailored for detection of COVID-19 cases from chest CT images. We also introduce COVIDx-CT, a CT image dataset comprising 104,009 images across 1,489 patient cases. Finally, we leverage explainability to investigate the decision-making behaviour of COVIDNet-CT and ensure that COVIDNet-CT makes predictions based on relevant indicators in CT images.

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