ArticleApplied intelligence (Dordrecht, Netherlands)2021
Deep learning based detection and analysis of COVID-19 on chest X-ray images.
Article in Applied intelligence (Dordrecht, Netherlands), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 128 papers, 3 of them syntheses that pooled it.
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Who cites it
128 citing papers in PubMed, 3 syntheses or guidelines pooled it, 517 citations in OpenAlex.
- Combating COVID-19 Crisis using Artificial Intelligence (AI) Based Approach: Systematic Review.Current topics in medicinal chemistry · 2024Pooled it
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- X-Ray Equipped with Artificial Intelligence: Changing the COVID-19 Diagnostic Paradigm during the Pandemic.BioMed research international · 2021Pooled it
- Enhancing generative networks for chest anomaly localization through automatic registration-based unpaired-to-pseudo-paired training data translation.Scientific reports · 2026Article
- An optimized multi-task contrastive learning framework for HIFU lesion detection and segmentation.Scientific reports · 2025Article
- Leveraging federated learning and edge computing for pandemic-resilient healthcare.Scientific reports · 2025Article
- Neural networks to model COVID-19 dynamics and allocate healthcare resources.Scientific reports · 2025Article
- U-FDL-PPE: a unified federated deep learning framework with privacy-preserving explainability for early and accurate viral disease prediction.Frontiers in radiology · 2025Article
- Comparison of radiological interpretation made by veterinary radiologists and state-of-the-art commercial AI software for canine and feline radiographic studies.Frontiers in veterinary science · 2025Article
- Medical image analysis using improved SAM-Med2D: segmentation and classification perspectives.BMC medical imaging · 2024Article
- AI-Driven Thoracic X-ray Diagnostics: Transformative Transfer Learning for Clinical Validation in Pulmonary Radiography.Journal of personalized medicine · 2024Article
- Challenges issues and future recommendations of deep learning techniques for SARS-CoV-2 detection utilising X-ray and CT images: a comprehensive review.PeerJ. Computer science · 2024Article
- RETRACTED: Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds.PloS one · 2024Article
- COVID-19 and Pneumonia detection and web deployment from CT scan and X-ray images using deep learning.PloS one · 2024Article
- COVID-19 Detection via Ultra-Low-Dose X-ray Images Enabled by Deep Learning.Bioengineering (Basel, Switzerland) · 2023Article
- Parallel CNN-ELM: A multiclass classification of chest X-ray images to identify seventeen lung diseases including COVID-19.Expert systems with applications · 2023Article
- Discovering associations between radiological features and COVID-19 patients' deterioration.Health science reports · 2023Article
- Joint Diagnosis of Pneumonia, COVID-19, and Tuberculosis from Chest X-ray Images: A Deep Learning Approach.Diagnostics (Basel, Switzerland) · 2023Article
- Detection of various lung diseases including COVID-19 using extreme learning machine algorithm based on the features extracted from a lightweight CNN architecture.Biocybernetics and biomedical engineering · 2023Article
- A survey of machine learning-based methods for COVID-19 medical image analysis.Medical & biological engineering & computing · 2023Review
68 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Covid-19 is a rapidly spreading viral disease that infects not only humans, but animals are also infected because of this disease. The daily life of human beings, their health, and the economy of a country are affected due to this deadly viral disease. Covid-19 is a common spreading disease, and till now, not a single country can prepare a vaccine for COVID-19. A clinical study of COVID-19 infected patients has shown that these types of patients are mostly infected from a lung infection after coming in contact with this disease. Chest x-ray (i.e., radiography) and chest CT are a more effective imaging technique for diagnosing lunge related problems. Still, a substantial chest x-ray is a lower cost process in comparison to chest CT. Deep learning is the most successful technique of machine learning, which provides useful analysis to study a large amount of chest x-ray images that can critically impact on screening of Covid-19. In this work, we have taken the PA view of chest x-ray scans for covid-19 affected patients as well as healthy patients. After cleaning up the images and applying data augmentation, we have used deep learning-based CNN models and compared their performance. We have compared Inception V3, Xception, and ResNeXt models and examined their accuracy. To analyze the model performance, 6432 chest x-ray scans samples have been collected from the Kaggle repository, out of which 5467 were used for training and 965 for validation. In result analysis, the Xception model gives the highest accuracy (i.e., 97.97%) for detecting Chest X-rays images as compared to other models. This work only focuses on possible methods of classifying covid-19 infected patients and does not claim any medical accuracy.
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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.