Chest X-ray image denoising method based on deep convolution neural network

2019 ◽  
Vol 13 (11) ◽  
pp. 1970-1978 ◽  
Author(s):  
Yan Jin ◽  
Xiao-Ben Jiang ◽  
Zhen-kun Wei ◽  
Yuan Li
Keyword(s):  
Neural Network ◽  
Image Denoising ◽  
Convolution Neural Network ◽  
Denoising Method ◽  
X Ray ◽  
Deep Convolution Neural Network ◽  
Chest X Ray

Image denoising method based on a deep convolution neural network

2018 ◽  
Vol 12 (4) ◽  
pp. 485-493 ◽  
Author(s):  
Fu Zhang ◽  
Nian Cai ◽  
Jixiu Wu ◽  
Guandong Cen ◽  
Han Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Denoising ◽  
Convolution Neural Network ◽  
Denoising Method ◽  
Deep Convolution Neural Network

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.

scholarly journals Modified AlexNet Convolution Neural Network For Covid-19 Detection Using Chest X-ray Images

2020 ◽  
pp. 119-130
Author(s):  
Shadman Q. Salih ◽  
Hawre Kh. Abdulla ◽  
Zanear Sh. Ahmed ◽  
Nigar M. Shafiq Surameery ◽  
Rasper Dh. Rashid
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Imaging Techniques ◽  
Convolution Neural Network ◽  
World Health ◽  
X Ray ◽  
Chest X Ray ◽  
Health Organization ◽  
Fully Connected ◽  
The City

First outbreak of COVID-19 was in the city of Wuhan in China in Dec.2019 and then it becomes a pandemic disease all around the world. World Health Organization (WHO) confirmed more than 5.5 million cases and 341,155 deaths from the disease till the time of writing this paper. This new worldwide disease forced researchers to make more precise way to diagnose COVID-19. In the last decade, medical imaging techniques show its efficiency in helping radiologists to detect and diagnose the diseases. Deep learning and transfer learning algorithms are good techniques to detect disease from different image source types such as X-Ray and CT scan images. In this work we used a deep learning technique based on Convolution Neural Network (CNN) to detect and diagnose COVID-19 disease using Chest X-ray images.  Moreover, the modified AlexNet architecture is proposed in different scenarios were differing from each other in terms of the type of the pooling layers and/or the number of the neurons that have used in the second fully connected layer. The used chest X-ray images are gathered from two COVID-19 X-ray image datasets and one dataset includes large number of normal and pneumonia X-ray images. With the proposed models we obtained the same or even better result than the original AlexNet with having a smaller number of neurons in the second fully connected layer.

Implementation of 1D-Convolution Neural Network for Pneumonia Classification Based Chest X-Ray Image

2019 ◽  
pp. 181-191
Author(s):  
Muhamad Fathurahman ◽  
Sri Cahya Fauzi ◽  
Sri Chusri Haryanti ◽  
Ummi Azizah Rahmawati ◽  
Elan Suherlan
Keyword(s):  
Neural Network ◽  
Convolution Neural Network ◽  
X Ray ◽  
Chest X Ray

scholarly journals Diagnosa COVID-19 Chest X-Ray Dengan Convolution Neural Network Arsitektur Resnet-152

2021 ◽  
Vol 2 (1) ◽  
pp. 26-33
Author(s):  
Widi Hastomo
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Viral Pneumonia ◽  
Convolution Neural Network ◽  
Normal Lung ◽  
X Ray ◽  
Image Dataset ◽  
Chest X Ray ◽  
Medical Aids

The availability of medical aids in adequate quantities is very much needed to assist the work of the medical staff in dealing with the very large number of Covid patients. Artificial Intelligence (AI) with the Deep Learning (DL) method, especially the Convolution Neural Network (CNN), is able to diagnose Chest X-ray images generated by the Computer Tomography Scanner (C.T. Scan) against certain diseases (Covid). Resnet Version-152 architecture was used in this study to train a dataset of 10.300 images, consisting of 4 classifications namely covid, normal, lung opacity with 3,000 images each and viral pneumonia 1,000 images. The results of the study with 50 epoch training obtained very good values for the accuracy of training and validation of 95.5% and 91.8%, respectively. The test with 10.300 image dataset obtained 98% accuracy testing, with the precision of each class being Covid (99%), Lung_Opacity (99%), Normal (98%) and Viral pneumonia (98%). 

scholarly journals An image denoising method based on BP neural network optimized by improved whale optimization algorithm

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Chunzhi Wang ◽  
Min Li ◽  
Ruoxi Wang ◽  
Han Yu ◽  
Shuping Wang
Keyword(s):  
Neural Network ◽  
Image Denoising ◽  
Optimization Algorithm ◽  
Bp Neural Network ◽  
Weight Coefficient ◽  
Whale Optimization Algorithm ◽  
Denoising Method ◽  
Training Time ◽  
Whale Optimization ◽  
City Construction

AbstractAs an important part of smart city construction, traffic image denoising has been studied widely. Image denoising technique can enhance the performance of segmentation and recognition model and improve the accuracy of segmentation and recognition results. However, due to the different types of noise and the degree of noise pollution, the traditional image denoising methods generally have some problems, such as blurred edges and details, loss of image information. This paper presents an image denoising method based on BP neural network optimized by improved whale optimization algorithm. Firstly, the nonlinear convergence factor and adaptive weight coefficient are introduced into the algorithm to improve the optimization ability and convergence characteristics of the standard whale optimization algorithm. Then, the improved whale optimization algorithm is used to optimize the initial weight and threshold value of BP neural network to overcome the dependence in the construction process, and shorten the training time of the neural network. Finally, the optimized BP neural network is applied to benchmark image denoising and traffic image denoising. The experimental results show that compared with the traditional denoising methods such as Median filtering, Neighborhood average filtering and Wiener filtering, the proposed method has better performance in peak signal-to-noise ratio.

scholarly journals An Automated Lightweight Deep Neural Network for Diagnosis of COVID-19 from Chest X-ray Images

2021 ◽  
Author(s):  
Soumya Ranjan Nayak ◽  
Janmenjoy Nayak ◽  
Utkarsh Sinha ◽  
Vaibhav Arora ◽  
Uttam Ghosh ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
X Ray ◽  
Chest X Ray
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