ArticleIEEE access : practical innovations, open solutions2020
Deep Convolutional Approaches for the Analysis of COVID-19 Using Chest X-Ray Images From Portable Devices.
Article in IEEE access : practical innovations, open solutions, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed, 90 citations in OpenAlex.
- Computer-based quantitative image texture analysis using multi-collinearity diagnosis in chest X-ray images.PloS one · 2025Article
- Efficient clinical decision-making process via AI-based multimodal data fusion: A COVID-19 case study.Heliyon · 2024Article
- Comprehensive analysis of clinical data for COVID-19 outcome estimation with machine learning models.Biomedical signal processing and control · 2023Article
- Biases associated with database structure for COVID-19 detection in X-ray images.Scientific reports · 2023Article
- Optimal Ensemble learning model for COVID-19 detection using chest X-ray images.Biomedical signal processing and control · 2023Article
- UncertaintyFuseNet: Robust uncertainty-aware hierarchical feature fusion model with Ensemble Monte Carlo Dropout for COVID-19 detection.An international journal on information fusion · 2023Article
- A novel framework based on deep learning for COVID-19 diagnosis from X-ray images.PeerJ. Computer science · 2023Article
- A robust semantic lung segmentation study for CNN-based COVID-19 diagnosis.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2022Article
- EVAE-Net: An Ensemble Variational Autoencoder Deep Learning Network for COVID-19 Classification Based on Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2022Article
- Advances in nanotechnology application in biosafety materials: A crucial response to COVID-19 pandemic.Biosafety and health · 2022Review
- CXGNet: A tri-phase chest X-ray image classification for COVID-19 diagnosis using deep CNN with enhanced grey-wolf optimizer.Biomedical signal processing and control · 2022Article
- Efficacy of Transfer Learning-based ResNet models in Chest X-ray image classification for detecting COVID-19 Pneumonia.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2022Article
- Does imbalance in chest X-ray datasets produce biased deep learning approaches for COVID-19 screening?BMC medical research methodology · 2022Article
- Automatic coronavirus disease 2019 diagnosis based on chest radiography and deep learning - Success story or dataset bias?Medical physics · 2022Article
- Survey on Diagnosing CORONA VIRUS from Radiography Chest X-ray Images Using Convolutional Neural Networks.Wireless personal communications · 2022Article
- Ensemble Deep Learning and Internet of Things-Based Automated COVID-19 Diagnosis Framework.Contrast media & molecular imaging · 2022Article
- Adaptive UNet-based Lung Segmentation and Ensemble Learning with CNN-based Deep Features for Automated COVID-19 Diagnosis.Multimedia tools and applications · 2022Article
- Data augmentation approaches using cycle-consistent adversarial networks for improving COVID-19 screening in portable chest X-ray images.Expert systems with applications · 2021Article
- Transfer Learning for the Detection and Diagnosis of Types of Pneumonia including Pneumonia Induced by COVID-19 from Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2021Article
- Multi-stage transfer learning for lung segmentation using portable X-ray devices for patients with COVID-19.Expert systems with applications · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The recent human coronavirus disease (COVID-19) is a respiratory infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Given the effects of COVID-19 in pulmonary tissues, chest radiography imaging plays an important role in the screening, early detection, and monitoring of the suspected individuals. Hence, as the pandemic of COVID-19 progresses, there will be a greater reliance on the use of portable equipment for the acquisition of chest X-ray images due to its accessibility, widespread availability, and benefits regarding to infection control issues, minimizing the risk of cross-contamination. This work presents novel fully automatic approaches specifically tailored for the classification of chest X-ray images acquired by portable equipment into 3 different clinical categories: normal, pathological, and COVID-19. For this purpose, 3 complementary deep learning approaches based on a densely convolutional network architecture are herein presented. The joint response of all the approaches allows to enhance the differentiation between patients infected with COVID-19, patients with other diseases that manifest characteristics similar to COVID-19 and normal cases. The proposed approaches were validated over a dataset specifically retrieved for this research. Despite the poor quality of the chest X-ray images that is inherent to the nature of the portable equipment, the proposed approaches provided global accuracy values of 79.62%, 90.27% and 79.86%, respectively, allowing a reliable analysis of portable radiographs to facilitate the clinical decision-making process.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.