scholarly journals CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images

2020 ◽  
Vol 196 ◽  
pp. 105581 ◽  
Author(s):  
Asif Iqbal Khan ◽  
Junaid Latief Shah ◽  
Mohammad Mudasir Bhat
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
X Ray ◽  
Chest X Ray ◽  
Detection And Diagnosis

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.

scholarly journals CCBlock based on deep learning for diagnosis COVID-19 in chest x-ray image

2020 ◽  
Author(s):  
Ali Al-Bawi ◽  
Karrar Ali Al-Kaabi ◽  
Mohammed Jeryo ◽  
Ahmad Al-Fatlawi
Keyword(s):  
Neural Network ◽  
Well Being ◽  
Healthy People ◽  
Alternative Methods ◽  
Pcr Analysis ◽  
Early Screening ◽  
Data Set ◽  
X Ray ◽  
Chest X Ray ◽  
Detection And Diagnosis

Abstract Purpose: COVID-19 pandemic continues to hit countries one after the other and has dramatically affected the health and well-being of the world's population. With the daily increase in the number of people with this disease, the impressive speed of spread and the delay in the results of PCR analysis, it may cause the disease to spread more broadly. Therefore it is necessary to consider finding alternative methods of detection and diagnosis COVID-19 to prohibit the spread of the disease among people. Convolutional Neural Network (CNN) automated detection systems have shown auspicious results in detecting patients with COVID-19 through radiography; thus, we suggest them as an alternative option to diagnose COVID-19.Method: In this study, an early screening model based on the enhancement of classical Visual Geometry Group Network (VGG) with Convolutional Covid Block (CCBlock) was proposed to detect and distinguish COVID-19 from Pneumonia, and healthy people using chest X-ray radiographs. The data set used for model testing is the x-ray images available on public platforms, which consist of 1,828 x-ray images, including 310 images for confirmed COVID-19 patients, 864 images for pneumonia patients, and 654 images for healthy people.Results: The experiment result of the dataset showed that the added enhancements to the classical VGG network with X-ray imaging provide the highest detection performance and overall accuracy of 98.52% for two classes and 95.34% accuracy for three classes.Conclusions: Considering the achievement results obtained, it was found that utilizing the enhanced VGG deep neural network helps radiologists automatically diagnose COVID-19 in X-ray images.

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.

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