Algorithm improvement of neural network in endoscopic image recognition of upper digestive tract system

2021 ◽  
Author(s):  
Bin Lu
Keyword(s):  
Neural Network ◽  
Digestive Tract ◽  
Image Recognition ◽  
Upper Digestive Tract ◽  
Endoscopic Image

scholarly journals Endoscopic Image-guided treatment of Upper Gastrointestinal foreign body and nursing care of complications

2021 ◽  
Vol 37 (6-WIT) ◽  
Author(s):  
Na Liu
Keyword(s):  
Foreign Body ◽  
Success Rate ◽  
Digestive Tract ◽  
Nursing Care ◽  
Foreign Bodies ◽  
Nursing Intervention ◽  
Image Guided ◽  
Upper Digestive Tract ◽  
Endoscopic Image ◽  
Upper Gastrointestinal

Objective: This study used phased array imaging algorithm to explore the epidemiological characteristics of endoscopic treatment of upper gastrointestinal foreign bodies to provide a basis for nursing intervention. Methods: We collected data on the age, sex, cause, type of foreign body, success rate of removal, retention location, time and complications of patients with foreign bodies in the upper gastrointestinal tract who were treated in the emergency department of the Digestive Endoscopy Center in our hospital. The study was conducted from January 2018 to December 2020 and we also performed statistical analysis. Results: The high incidence of foreign bodies in the upper digestive tract was in 45 years old to 74 years old patients. The foreign body types were mostly food balls and sharp foreign bodies, accounting for 37.0% and 44.2%, respectively. The cause was misuse and the most accounted for 52.1%, followed by oesophageal pathological stenosis which accounted for 45.5%. The oesophagus in the retention site accounted for up to 80.0%, and the success rate of foreign body extraction was 96.4%. The complications of patients with foreign body retention within twenty four our retention were mainly esophageal scratches and traumatic esophagitis, accounting for 48.5%. 39.6%. Conclusion: There are high risks in the treatment of foreign bodies in the upper digestive tract. Targeted, prospective, and streamlined nursing interventions can provide patients with fast and professional medical care services and minimize patient pain. doi: https://doi.org/10.12669/pjms.37.6-WIT.4858 How to cite this:Liu N. Endoscopic Image-guided treatment of Upper Gastrointestinal foreign body and nursing care of complications. Pak J Med Sci. 2021;37(6):1636-1640. doi: https://doi.org/10.12669/pjms.37.6-WIT.4858 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

NEURAL NETWORK FOR IMAGE RECOGNITION OF MAGNETOOPTIC MATERIALS DOMAIN STRUCTURES

2016 ◽  
Vol 59 (3) ◽  
pp. 117-121
Author(s):  
A. V. Bragin ◽  
◽  
R. R. Navletov ◽  
D. V. Pyanzin ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Image Recognition ◽  
Domain Structures

Unmanned aerial vehicle reconnaissance image recognition based on convolutional neural network

2021 ◽  
pp. 1-10
Author(s):  
Lipeng Si ◽  
Baolong Liu ◽  
Yanfang Fu
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Image Recognition ◽  
Error Detection ◽  
Convolution Neural Network ◽  
Data Sets ◽  
Small Target ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Real Time Tracking

The important strategic position of military UAVs and the wide application of civil UAVs in many fields, they all mark the arrival of the era of unmanned aerial vehicles. At present, in the field of image research, recognition and real-time tracking of specific objects in images has been a technology that many scholars continue to study in depth and need to be further tackled. Image recognition and real-time tracking technology has been widely used in UAV aerial photography. Through the analysis of convolution neural network algorithm and the comparison of image recognition technology, the convolution neural network algorithm is improved to improve the image recognition effect. In this paper, a target detection technique based on improved Faster R-CNN is proposed. The algorithm model is implemented and the classification accuracy is improved through Faster R-CNN network optimization. Aiming at the problem of small target error detection and scale difference in aerial data sets, this paper designs the network structure of RPN and the optimization scheme of related algorithms. The structure of Faster R-CNN is adjusted by improving the embedding of CNN and OHEM algorithm, the accuracy of small target and multitarget detection is improved as a whole. The experimental results show that: compared with LENET-5, the recognition accuracy of the proposed algorithm is significantly improved. And with the increase of the number of samples, the accuracy of this algorithm is 98.9%.

scholarly journals Adaptive hyperparameter updating for training restricted Boltzmann machines on quantum annealers

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Guanglei Xu ◽  
William S. Oates
Keyword(s):  
Neural Network ◽  
Maximum Likelihood ◽  
Image Reconstruction ◽  
Image Recognition ◽  
Shannon Entropy ◽  
Reconstruction Error ◽  
Likelihood Method ◽  
Restricted Boltzmann Machines ◽  
Boltzmann Machines ◽  
D Wave

AbstractRestricted Boltzmann Machines (RBMs) have been proposed for developing neural networks for a variety of unsupervised machine learning applications such as image recognition, drug discovery, and materials design. The Boltzmann probability distribution is used as a model to identify network parameters by optimizing the likelihood of predicting an output given hidden states trained on available data. Training such networks often requires sampling over a large probability space that must be approximated during gradient based optimization. Quantum annealing has been proposed as a means to search this space more efficiently which has been experimentally investigated on D-Wave hardware. D-Wave implementation requires selection of an effective inverse temperature or hyperparameter ($$\beta $$ β ) within the Boltzmann distribution which can strongly influence optimization. Here, we show how this parameter can be estimated as a hyperparameter applied to D-Wave hardware during neural network training by maximizing the likelihood or minimizing the Shannon entropy. We find both methods improve training RBMs based upon D-Wave hardware experimental validation on an image recognition problem. Neural network image reconstruction errors are evaluated using Bayesian uncertainty analysis which illustrate more than an order magnitude lower image reconstruction error using the maximum likelihood over manually optimizing the hyperparameter. The maximum likelihood method is also shown to out-perform minimizing the Shannon entropy for image reconstruction.

scholarly journals A Review of Plant Phenotypic Image Recognition Technology Based on Deep Learning

Electronics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 81
Author(s):  
Jianbin Xiong ◽  
Dezheng Yu ◽  
Shuangyin Liu ◽  
Lei Shu ◽  
Xiaochan Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Plant Species ◽  
Image Recognition ◽  
Recurrent Neural Network ◽  
Plant Diseases ◽  
Learning Methods ◽  
Smart Agriculture ◽  
Important Branch

Plant phenotypic image recognition (PPIR) is an important branch of smart agriculture. In recent years, deep learning has achieved significant breakthroughs in image recognition. Consequently, PPIR technology that is based on deep learning is becoming increasingly popular. First, this paper introduces the development and application of PPIR technology, followed by its classification and analysis. Second, it presents the theory of four types of deep learning methods and their applications in PPIR. These methods include the convolutional neural network, deep belief network, recurrent neural network, and stacked autoencoder, and they are applied to identify plant species, diagnose plant diseases, etc. Finally, the difficulties and challenges of deep learning in PPIR are discussed.

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