Metal Artifact Recognition using Deep Convolutional Neural Network in Abdomen and Pelvis CT Image

2019 ◽  
Author(s):  
Rattasart Sakunrat ◽  
Kongphum Arthamanolap ◽  
Pattarapong Phasukkit
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Metal Artifact ◽  
Ct Image

scholarly journals A new deep convolutional neural network design with efficient learning capability: Application to CT image synthesis from MRI

2020 ◽  
Vol 47 (10) ◽  
pp. 5158-5171 ◽  
Author(s):  
Abass Bahrami ◽  
Alireza Karimian ◽  
Emad Fatemizadeh ◽  
Hossein Arabi ◽  
Habib Zaidi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Design ◽  
Image Synthesis ◽  
Deep Convolutional Neural Network ◽  
Ct Image ◽  
Learning Capability ◽  
Efficient Learning

LENS CLASSIFICATION ACCORDING TO THE TYPE OF LIGHT SPOT USING A NEURAL NETWORK

2020 ◽  
Vol 2020 (4) ◽  
pp. 4-14
Author(s):  
Vladimir Budak ◽  
Ekaterina Ilyina
Keyword(s):  
Neural Network ◽  
Light Intensity ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Beam Angle ◽  
New Model ◽  
Technical Parameters ◽  
Transfer Training ◽  
Trained Network

The article proposes the classification of lenses with different symmetrical beam angles and offers a scale as a spot-light’s palette. A collection of spotlight’s images was created and classified according to the proposed scale. The analysis of 788 pcs of existing lenses and reflectors with different LEDs and COBs carried out, and the dependence of the axial light intensity from beam angle was obtained. A transfer training of new deep convolutional neural network (CNN) based on the pre-trained GoogleNet was performed using this collection. GradCAM analysis showed that the trained network correctly identifies the features of objects. This work allows us to classify arbitrary spotlights with an accuracy of about 80 %. Thus, light designer can determine the class of spotlight and corresponding type of lens with its technical parameters using this new model based on CCN.

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