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

scholarly journals MFDNN: Multi-channel feature deep neural network algorithm to identify Covid19 chest X-ray images

2021 ◽  
Author(s):  
Liangrui Pan ◽  
boya ji ◽  
Xiaoqi wang ◽  
shaoliang peng
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Test Accuracy ◽  
Learning Networks ◽  
X Ray ◽  
Network Algorithm ◽  
Life Saving ◽  
Chest X Ray ◽  
Neural Network Algorithm

The use of chest X-ray images (CXI) to detect Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2) caused by Coronavirus Disease 2019 (COVID-19) is life-saving important for both patients and doctors. This research proposed a multi-channel feature deep neural network algorithm to screen people infected with COVID-19. The algorithm integrates data oversampling technology and a multi-channel feature deep neural network model to carry out the training process in an end-to-end manner. In the experiment, we used a publicly available CXI database with 10,192 Normal, 6012 Lung Opacity (Non-COVID lung infection), and 1345 Viral Pneumonia images. Compared with traditional deep learning models (Densenet201, ResNet50, VGG19, GoogLeNet), the MFDNN model obtains an average test accuracy of 93.19% in all data. Furthermore, in each type of screening, the precision, recall, and F1 Score of the MFDNN model are also better than traditional deep learning networks. Secondly, compared with the latest CoroDet model, the MFDNN algorithm is 1.91% higher than the CoroDet model in the experiment of detecting the four categories of COVID19 infected persons. Finally, our experimental code will be placed at https://github.com/panliangrui/covid19.

Deep neural network ensemble for pneumonia localization from a large-scale chest x-ray database

2019 ◽  
Vol 78 ◽  
pp. 388-399 ◽  
Author(s):  
Ilyas Sirazitdinov ◽  
Maksym Kholiavchenko ◽  
Tamerlan Mustafaev ◽  
Yuan Yixuan ◽  
Ramil Kuleev ◽  
...  
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Deep Neural Network ◽  
Neural Network Ensemble ◽  
X Ray ◽  
Chest X Ray

scholarly journals HOG + CNN Net: Diagnosing COVID-19 and Pneumonia by Deep Neural Network from Chest X-Ray Images

2021 ◽  
Vol 2 (5) ◽  
Author(s):  
Mohammad Marufur Rahman ◽  
Sheikh Nooruddin ◽  
K. M. Azharul Hasan ◽  
Nahin Kumar Dey
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
X Ray ◽  
Chest X Ray

Development of a deep neural network for generating synthetic dual-energy chest x-ray images with single x-ray exposure

2019 ◽  
Vol 64 (11) ◽  
pp. 115017 ◽  
Author(s):  
Donghoon Lee ◽  
Hwiyoung Kim ◽  
Byungwook Choi ◽  
Hee-Joung Kim
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Dual Energy ◽  
X Ray ◽  
Chest X Ray

scholarly journals Developing an efficient deep neural network for automatic detection of COVID-19 using chest X-ray images

2021 ◽  
Vol 60 (3) ◽  
pp. 2885-2903
Author(s):  
Sobhan Sheykhivand ◽  
Zohreh Mousavi ◽  
Sina Mojtahedi ◽  
Tohid Yousefi Rezaii ◽  
Ali Farzamnia ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Automatic Detection ◽  
X Ray ◽  
Chest X Ray

scholarly journals An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Farah E. Shamout ◽  
Yiqiu Shen ◽  
Nan Wu ◽  
Aakash Kaku ◽  
Jungkyu Park ◽  
...  
Keyword(s):  
Neural Network ◽  
Emergency Department ◽  
New York ◽  
Deep Neural Network ◽  
Characteristic Curve ◽  
Gradient Boosting ◽  
New York University ◽  
X Ray ◽  
Intelligence System ◽  
Chest X Ray

AbstractDuring the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3661 patients, achieves an area under the receiver operating characteristic curve (AUC) of 0.786 (95% CI: 0.745–0.830) when predicting deterioration within 96 hours. The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time. In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.

scholarly journals Improved COVID-19 detection using data augmentation Deep Convolution GAN and classifier DenseNet.

2021 ◽  
Author(s):  
Debmitra Ghosh
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Classification Accuracy ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Training Data ◽  
X Rays ◽  
X Ray ◽  
Chest X Ray ◽  
Synthetic Images

Abstract SARS-CoV-2 or severe acute respiratory syndrome coronavirus 2 is considered to be the cause of Coronavirus (COVID-19) which is a viral disease. The rapid spread of COVID-19 is having a detrimental effect on the global economy and health. A chest X-ray of infected patients can be considered as a crucial step in the battle against COVID-19. On retrospections, it is found that abnormalities exist in chest X-rays of patients suggestive of COVID-19. This sparked the introduction of a variety of deep learning systems and studies which have shown that the accuracy of COVID-19 patient detection through the use of chest X-rays is strongly optimistic. Although there are certain shortcomings like deep learning networks like convolutional neural networks (CNNs) need a substantial amount of training data but the outbreak is recent, so it is large datasets of radiographic images of the COVID-19 infected patients are not available in such a short time. Here, in this research, we present a method to generate synthetic chest X-ray (CXR) images by developing a Deep Convolution Generative Adversarial Network-based model. In addition, we demonstrate that the synthetic images produced from DCGAN can be utilized to enhance the performance of CNN for COVID-19 detection. Classification using CNN alone yielded 85% accuracy. Although there are several models available, we chose MobileNet as it is a lightweight deep neural network, with fewer parameters and higher classification accuracy. Here we are using a deep neural network-based model to diagnose COVID-19 infected patients through radiological imaging of 5,859 Chest X-Ray images. We are using a Deep Convolutional Neural Network and a pre-trained model “DenseNet 121” for two new label classes (COVID-19 and Normal). To improve the classification accuracy, in our work we have further reduced the number of network parameters by introducing dense blocks that are proposed in DenseNets into MobileNet. By adding synthetic images produced by DCGAN, the accuracy increased to 97%. Our goal is to use this method to speed up COVID-19 detection and lead to more robust systems of radiology.

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