ArticleIEEE access : practical innovations, open solutions2021
Deep Learning Approaches for Detecting COVID-19 From Chest X-Ray Images: A Survey.
Article in IEEE access : practical innovations, open solutions, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- CCEO-DCABNet: Chronological Chaotic Evolution Optimization-Enabled Hybrid Deep Learning for Multiclass Disease Classification Using Chest X-Ray Images in Federated Learning.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial Intelligence Methods in Infection Biology Research.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Efficient clinical decision-making process via AI-based multimodal data fusion: A COVID-19 case study.Heliyon · 2024Article
- Deep Learning for Pneumonia Detection in Chest X-ray Images: A Comprehensive Survey.Journal of imaging · 2024Review
- Toward the novel AI tasks in infection biology.mSphere · 2024Review
- Design and Validation of a Deep Learning Model for Renal Stone Detection and Segmentation on Kidney-Ureter-Bladder Images.Bioengineering (Basel, Switzerland) · 2023Article
- Lightweight deep CNN-based models for early detection of COVID-19 patients from chest X-ray images.Expert systems with applications · 2023Article
- A survey of machine learning-based methods for COVID-19 medical image analysis.Medical & biological engineering & computing · 2023Review
- SecureFed: federated learning empowered medical imaging technique to analyze lung abnormalities in chest X-rays.International journal of machine learning and cybernetics · 2023Article
- A radiographic, deep transfer learning framework, adapted to estimate lung opacities from chest x-rays.Bioelectronic medicine · 2023Article
- A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography.Diagnostics (Basel, Switzerland) · 2023Review
- A comprehensive review on variants of SARS-CoVs-2: Challenges, solutions and open issues.Computer communications · 2023Article
- Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review.SN computer science · 2023Article
- Deep Convolutional Neural Networks for Detecting COVID-19 Using Medical Images: A Survey.New generation computing · 2023Article
- An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works.Multimedia systems · 2023Article
- A review on lung disease recognition by acoustic signal analysis with deep learning networks.Journal of big data · 2023Article
- Preliminary Stages for COVID-19 Detection Using Image Processing.Diagnostics (Basel, Switzerland) · 2022Review
- Application of Deep Learning Techniques in Diagnosis of Covid-19 (Coronavirus): A Systematic Review.Neural processing letters · 2022Review
- 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
- Interactive framework for Covid-19 detection and segmentation with feedback facility for dynamically improved accuracy and trust.PloS one · 2022Article
Corrections and comments
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Chest X-ray (CXR) imaging is a standard and crucial examination method used for suspected cases of coronavirus disease (COVID-19). In profoundly affected or limited resource areas, CXR imaging is preferable owing to its availability, low cost, and rapid results. However, given the rapidly spreading nature of COVID-19, such tests could limit the efficiency of pandemic control and prevention. In response to this issue, artificial intelligence methods such as deep learning are promising options for automatic diagnosis because they have achieved state-of-the-art performance in the analysis of visual information and a wide range of medical images. This paper reviews and critically assesses the preprint and published reports between March and May 2020 for the diagnosis of COVID-19 via CXR images using convolutional neural networks and other deep learning architectures. Despite the encouraging results, there is an urgent need for public, comprehensive, and diverse datasets. Further investigations in terms of explainable and justifiable decisions are also required for more robust, transparent, and accurate predictions.
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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.