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 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 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

scholarly journals Tinjauan Literatur Sistematik tentang Deteksi Covid-19 menggunakan Convolutional Neural Networks

2021 ◽  
Vol 8 (1) ◽  
pp. 9
Author(s):  
Buyut Khoirul Umri ◽  
Ema Utami ◽  
Mei P Kurniawan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Literature Review ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Large Scale ◽  
Image Data ◽  
X Rays ◽  
X Ray ◽  
Chest X Ray

Covid-19 menyerang sel-sel epitel yang melapisi saluran pernapasan sehingga dalam kasus ini dapat memanfaatkan gambar x-ray dada untuk menganalisis kesehatan paru-paru pada pasien. Menggunakan x-ray dalam bidang medis merupakan metode yang lebih cepat, lebih mudah dan tidak berbahaya yang dapat dimanfaatkan pada banyak hal. Salah satu metode yang paling sering digunakan dalam klasifikasi gambar adalah convolutional neural networks (CNN). CNN merupahan jenis neural network yang sering digunakan dalam data gambar dan sering digunakan dalam mendeteksi dan mengenali object pada sebuah gambar. Model arsitektur pada metode CNN juga dapat dikembangkan dengan transfer learning yang merupakan proses menggunakan kembali model pre-trained yang dilatih pada dataset besar, biasanya pada tugas klasifikasi gambar berskala besar. Tinjauan literature review ini digunakan untuk menganalisis penggunaan transfer learning pada CNN sebagai metode yang dapat digunakan untuk mendeteksi covid-19 pada gambar x-ray dada. Hasil sistematis review menunjukkan bahwa algoritma CNN dapat digunakan dengan akruasi yang baik dalam mendeteksi covid-19 pada gambar x-ray dada dan dengan pengembangan model transfer learning mampu mendapatkan performa yang maksimal dengan dataset yang besar maupun kecil.Kata Kunci—CNN, transfer learning, deteksi, covid-19Covid-19 attacks the epithelial cells lining the respiratory tract so that in this case it can utilize chest x-ray images to analyze the health of the lungs in patients. Using x-rays in the medical field is a faster, easier and harmless method that can be utilized in many ways. One of the most frequently used methods in image classification is convolutional neural networks (CNN). CNN is a type of neural network that is often used in image data and is often used in detecting and recognizing objects in an image. The architectural model in the CNN method can also be developed with transfer learning which is the process of reusing pre-trained models that are trained on large datasets, usually on the task of classifying large-scale images. This literature review review is used to analyze the use of transfer learning on CNN as a method that can be used to detect covid-19 on chest x-ray images. The systematic review results show that the CNN algorithm can be used with good accuracy in detecting covid-19 on chest x-ray images and by developing transfer learning models able to get maximum performance with large and small datasets.Keywords—CNN, transfer learning, detection, covid-19

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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