scholarly journals A deep learning model to screen for Corona Virus Disease (COVID-19) from X-ray chest images

2020 ◽  
Author(s):  
Enrique Pelaez ◽  
Francis Loayza
Keyword(s):  
Deep Learning ◽  
Virus Disease ◽  
Learning Model ◽  
X Ray ◽  
Corona Virus ◽  
Deep Learning Model

scholarly journals Accessory pathway analysis using a multimodal deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Makoto Nishimori ◽  
Kunihiko Kiuchi ◽  
Kunihiro Nishimura ◽  
Kengo Kusano ◽  
Akihiro Yoshida ◽  
...  
Keyword(s):  
Deep Learning ◽  
Electrophysiological Study ◽  
Accessory Pathway ◽  
Learning Model ◽  
X Rays ◽  
X Ray ◽  
Wpw Syndrome ◽  
Chest X Ray ◽  
Deep Learning Model ◽  
Ecg Data

AbstractCardiac accessory pathways (APs) in Wolff–Parkinson–White (WPW) syndrome are conventionally diagnosed with decision tree algorithms; however, there are problems with clinical usage. We assessed the efficacy of the artificial intelligence model using electrocardiography (ECG) and chest X-rays to identify the location of APs. We retrospectively used ECG and chest X-rays to analyse 206 patients with WPW syndrome. Each AP location was defined by an electrophysiological study and divided into four classifications. We developed a deep learning model to classify AP locations and compared the accuracy with that of conventional algorithms. Moreover, 1519 chest X-ray samples from other datasets were used for prior learning, and the combined chest X-ray image and ECG data were put into the previous model to evaluate whether the accuracy improved. The convolutional neural network (CNN) model using ECG data was significantly more accurate than the conventional tree algorithm. In the multimodal model, which implemented input from the combined ECG and chest X-ray data, the accuracy was significantly improved. Deep learning with a combination of ECG and chest X-ray data could effectively identify the AP location, which may be a novel deep learning model for a multimodal model.

Detecting COVID-19 infected pneumonia from x-ray images using a deep learning model with image preprocessing algorithm

2021 ◽  
Author(s):  
Morteza Heidari ◽  
Seyedehnafiseh Mirniaharikandehei ◽  
Abolfazl Zargari Khuzani ◽  
Gopichandh Danala ◽  
Yuchen Qiu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Image Preprocessing ◽  
X Ray ◽  
Deep Learning Model

scholarly journals Pneumonia detection in chest X-ray images using compound scaled deep learning model

Automatika ◽  
2021 ◽  
Vol 62 (3-4) ◽  
pp. 397-406
Author(s):  
Mohammad Farukh Hashmi ◽  
Satyarth Katiyar ◽  
Abdul Wahab Hashmi ◽  
Avinash G. Keskar
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
X Ray ◽  
Chest X Ray ◽  
Deep Learning Model

scholarly journals CoviDecode : Detection of COVID-19 from Chest X-Ray images using Convolutional Neural Networks

2020 ◽  
Vol 6 (12) ◽  
pp. 436-439
Author(s):  
Rishabh Raj
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Statistical Data ◽  
Virus Disease ◽  
X Ray ◽  
Corona Virus ◽  
Life Saving ◽  
Chest X Ray

ommand, product recommendation and medical diagnosis. The detection of severe acute respiratory syndrome corona virus 2 (SARS CoV-2), which is responsible for corona virus disease 2019 (COVID-19), using chest X-ray images has life-saving importance for bothpatients and doctors. In addition, in countries that are unable to purchase laboratory kits for testing, this becomes even more vital. In this study, we aimed to present the use of deep learning for the high-accuracy detection of COVID-19 using chest X-ray images. Publicly available X-ray images were used in the experiments, which involved the training of deep learning and machine learning classifiers. Experiments were performed using convolutional neural networks and machine learning models. Images and statistical data were considered separately in the experiments to evaluate the performances of models, and eightfold cross-validation was used. A mean accuracy of 98.50%. A convolutional neural network without pre-processing and with minimized layers is capable of detecting COVID- 19 in a limited number of, and in imbalanced, chest X-rayimages.

Sign in / Sign up

Export Citation Format

Share Document