ArticlePattern recognition2021
Automatically discriminating and localizing COVID-19 from community-acquired pneumonia on chest X-rays.
Article in Pattern recognition, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05356494 (Comparisons Effects of Postural Drainage and Positive Expiratory Pressure Technique in Community Acquired Pneumonia.), which is not on this map. Cited by 66 papers, 1 of them a synthesis that pooled it.
What it found
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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.
Comparisons Effects of Postural Drainage and Positive Expiratory Pressure Technique in Community Acquired Pneumonia.
Who cites it
66 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for imaging-based COVID-19 detection: Systematic review comparing added value of AI versus human readers.European journal of radiology · 2021Pooled it
- Inverse Dynamics Meets Markerless Motion Capture during Standing: Concurrent Validation of Center of Mass and Center of Pressure Estimations.Annals of biomedical engineering · 2026Article
- TriNet-MoE: One New Neural Network Framework Based on Mixture of Experts for CXR-Based COVID-19 Detection.Journal of imaging informatics in medicine · 2026Article
- Review
- Binary Classification of Pneumonia in Chest X-Ray Images Using Modified Contrast-Limited Adaptive Histogram Equalization Algorithm.Sensors (Basel, Switzerland) · 2025Article
- Article
- Interactive computer-aided diagnosis on medical image using large language models.Communications engineering · 2024Article
- Deep Learning for Pneumonia Detection in Chest X-ray Images: A Comprehensive Survey.Journal of imaging · 2024Review
- COVID-19 screening in low resource settings using artificial intelligence for chest radiographs and point-of-care blood tests.Scientific reports · 2023Article
- Deep Learning to Predict the Cell Proliferation and Prognosis of Non-Small Cell Lung Cancer Based on FDG-PET/CT Images.Diagnostics (Basel, Switzerland) · 2023Article
- PCovNet+: A CNN-VAE anomaly detection framework with LSTM embeddings for smartwatch-based COVID-19 detection.Engineering applications of artificial intelligence · 2023Article
- COVID-19 imaging, where do we go from here? Bibliometric analysis of medical imaging in COVID-19.European radiology · 2023Article
- Biases associated with database structure for COVID-19 detection in X-ray images.Scientific reports · 2023Article
- In Search of an Efficient and Reliable Deep Learning Model for Identification of COVID-19 Infection from Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2023Article
- Bilateral adaptive graph convolutional network on CT based Covid-19 diagnosis with uncertainty-aware consensus-assisted multiple instance learning.Medical image analysis · 2023Article
- Densely attention mechanism based network for COVID-19 detection in chest X-rays.Scientific reports · 2023Article
- Survey of Explainable AI Techniques in Healthcare.Sensors (Basel, Switzerland) · 2023Review
- ERGPNet: lesion segmentation network for COVID-19 chest X-ray images based on embedded residual convolution and global perception.Frontiers in physiology · 2023Article
- Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review.SN 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
6 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The COVID-19 outbreak continues to threaten the health and life of people worldwide. It is an immediate priority to develop and test a computer-aided detection (CAD) scheme based on deep learning (DL) to automatically localize and differentiate COVID-19 from community-acquired pneumonia (CAP) on chest X-rays. Therefore, this study aims to develop and test an efficient and accurate deep learning scheme that assists radiologists in automatically recognizing and localizing COVID-19. A retrospective chest X-ray image dataset was collected from open image data and the Xiangya Hospital, which was divided into a training group and a testing group. The proposed CAD framework is composed of two steps with DLs: the Discrimination-DL and the Localization-DL. The first DL was developed to extract lung features from chest X-ray radiographs for COVID-19 discrimination and trained using 3548 chest X-ray radiographs. The second DL was trained with 406-pixel patches and applied to the recognized X-ray radiographs to localize and assign them into the left lung, right lung or bipulmonary. X-ray radiographs of CAP and healthy controls were enrolled to evaluate the robustness of the model. Compared to the radiologists' discrimination and localization results, the accuracy of COVID-19 discrimination using the Discrimination-DL yielded 98.71%, while the accuracy of localization using the Localization-DL was 93.03%. This work represents the feasibility of using a novel deep learning-based CAD scheme to efficiently and accurately distinguish COVID-19 from CAP and detect localization with high accuracy and agreement with radiologists.
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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.