scholarly journals Detecting gastric cancer from video images using convolutional neural networks

2018 ◽  
Vol 31 (2) ◽  
Author(s):  
Mitsuaki Ishioka ◽  
Toshiaki Hirasawa ◽  
Tomohiro Tada
Keyword(s):  
Gastric Cancer ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Video Images

scholarly journals Applying Convolutional Neural Networks for Automatic Detection of Early Gastric Cancer with Limited Endoscopic Images

2019 ◽  
Vol 85 (9) ◽  
pp. 761-764
Author(s):  
Satoko TAKEMOTO ◽  
Keisuke HORI ◽  
Yoshimasa SAKAI ◽  
Masaomi NISHIMURA ◽  
Hiroaki IKEMATSU ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Neural Networks ◽  
Early Gastric Cancer ◽  
Convolutional Neural Networks ◽  
Automatic Detection ◽  
Endoscopic Images

scholarly journals End-to-end learning to predict survival in patients with gastric cancer using convolutional neural networks

2018 ◽  
Vol 29 ◽  
pp. viii23 ◽  
Author(s):  
A. Meier ◽  
K. Nekolla ◽  
S. Earle ◽  
L. Hewitt ◽  
T. Aoyama ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
End To End

scholarly journals Classifying Behaviours in Videos with Recurrent Neural Networks

2017 ◽  
Vol 7 (4) ◽  
pp. 1-15 ◽  
Author(s):  
Javier Abellan-Abenza ◽  
Alberto Garcia-Garcia ◽  
Sergiu Oprea ◽  
David Ivorra-Piqueres ◽  
Jose Garcia-Rodriguez
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Video Surveillance ◽  
Activity Recognition ◽  
Human Activity ◽  
Recurrent Neural Networks ◽  
Low Cost ◽  
Video Images ◽  
Employee Monitoring

This article describes how the human activity recognition in videos is a very attractive topic among researchers due to vast possible applications. This article considers the analysis of behaviors and activities in videos obtained with low-cost RGB cameras. To do this, a system is developed where a video is input, and produces as output the possible activities happening in the video. This information could be used in many applications such as video surveillance, disabled person assistance, as a home assistant, employee monitoring, etc. The developed system makes use of the successful techniques of Deep Learning. In particular, convolutional neural networks are used to detect features in the video images, meanwhile Recurrent Neural Networks are used to analyze these features and predict the possible activity in the video.

The Method of Classifying Fog Level of Outdoor Video Images Based on Convolutional Neural Networks

2021 ◽  
Author(s):  
Xiangwei Zhao ◽  
Jiaojiao Jiang ◽  
Kang Feng ◽  
Bo Wu ◽  
Jishan Luan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Video Images

scholarly journals Classifying Behaviours in Videos with Recurrent Neural Networks

2020 ◽  
pp. 965-980
Author(s):  
Javier Abellan-Abenza ◽  
Alberto Garcia-Garcia ◽  
Sergiu Oprea ◽  
David Ivorra-Piqueres ◽  
Jose Garcia-Rodriguez
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Video Surveillance ◽  
Activity Recognition ◽  
Human Activity ◽  
Recurrent Neural Networks ◽  
Low Cost ◽  
Video Images ◽  
Employee Monitoring

This article describes how the human activity recognition in videos is a very attractive topic among researchers due to vast possible applications. This article considers the analysis of behaviors and activities in videos obtained with low-cost RGB cameras. To do this, a system is developed where a video is input, and produces as output the possible activities happening in the video. This information could be used in many applications such as video surveillance, disabled person assistance, as a home assistant, employee monitoring, etc. The developed system makes use of the successful techniques of Deep Learning. In particular, convolutional neural networks are used to detect features in the video images, meanwhile Recurrent Neural Networks are used to analyze these features and predict the possible activity in the video.

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