Material classification using convolution neural network (CNN) for x-ray based coded aperture diffraction system

2019 ◽  
Author(s):  
Kris Brumbaugh ◽  
Camen Royse ◽  
Christopher Gregory ◽  
Kris Roe ◽  
Joel A. Greenberg ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolution Neural Network ◽  
Coded Aperture ◽  
X Ray ◽  
Material Classification ◽  
Aperture Diffraction

Towards an x-ray-based coded aperture diffraction system for bulk material identification

2018 ◽  
Author(s):  
Souleymane O. Diallo ◽  
Christopher Gregory ◽  
Kristofer Roe ◽  
Kamilah A. Tadlock ◽  
Joel A. Greenberg ◽  
...  
Keyword(s):  
Bulk Material ◽  
Coded Aperture ◽  
Material Identification ◽  
X Ray ◽  
Aperture Diffraction

Comparison of three reconstruction methods based on deconvolution, iterative algorithm and neural network for X-ray fluorescence imaging with coded aperture optics

2020 ◽  
Vol 35 (7) ◽  
pp. 1423-1434 ◽  
Author(s):  
Anico Kulow ◽  
Ana Guilherme Buzanich ◽  
Uwe Reinholz ◽  
Franziska Emmerling ◽  
Sven Hampel ◽  
...  
Keyword(s):  
Neural Network ◽  
Fluorescence Imaging ◽  
Iterative Algorithm ◽  
Coded Aperture ◽  
X Ray ◽  
Reconstruction Methods

Superimposed images acquired by XRF with coded aperture optics can be recovered successfully iteratively.

scholarly journals DenseCapsNet: Detection of COVID-19 X-ray Images Using a Capsule Network

2020 ◽  
Author(s):  
Hao Quan ◽  
Xiaosong Xu ◽  
Tingting Zheng ◽  
Zhi Li ◽  
Mingfang Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Data Augmentation ◽  
Detection Performance ◽  
Convolution Neural Network ◽  
X Ray ◽  
Learning Framework ◽  
X Ray Imaging ◽  
Chest X Ray ◽  
Novel Coronavirus

Abstract Objective: A deep learning framework for detecting COVID-19 is developed, and a small amount of chest X-ray data is used to accurately screen COVID-19.Methods: In this paper, we propose a deep learning framework that integrates convolution neural network and capsule network. DenseNet and CapsNet fusion are used to give full play to their respective advantages, reduce the dependence of convolution neural network on a large amount of data, and can quickly and accurately distinguish COVID-19 from Non-COVID-19 through chest X-ray imaging.Results: A total of 1472 chest X-ray COVID-19 and non-COVID-19 images are used, this method can achieve an accuracy of 99.32% and a precision of 100%, with 98.55% sensitivity and 100% specificity.Conclusion: These results show that the deep fusion neural network DenseCapsNet has good performance in novel coronavirus pneumonia X-ray detection. We also prove through experiments that the detection performance of DenseCapsNet is not affected fundamentally by a lack of data augmentation and pre-training.

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