ArticleJournal of biomolecular structure & dynamics2021
Using X-ray images and deep learning for automated detection of coronavirus disease.
Article in Journal of biomolecular structure & dynamics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 113 papers, 5 of them syntheses that pooled it.
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Who cites it
113 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Combating COVID-19 Crisis using Artificial Intelligence (AI) Based Approach: Systematic Review.Current topics in medicinal chemistry · 2024Pooled it
- Medical image processing and COVID-19: A literature review and bibliometric analysis.Journal of infection and public health · 2022Pooled it
- A Comprehensive Review of Artificial Intelligence in Prevention and Treatment of COVID-19 Pandemic.Frontiers in genetics · 2022Pooled it
- X-Ray Equipped with Artificial Intelligence: Changing the COVID-19 Diagnostic Paradigm during the Pandemic.BioMed research international · 2021Pooled it
- Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Systematic Review.Journal of healthcare engineering · 2021Pooled it
- Review
- Classification of pulmonary disorders using pulmonary network with explainable AI framework from chest multi-modal images.Scientific reports · 2026Article
- Cutting-edge bayesian deep learning and statistical strategies for bias mitigation in COVID-19 detection via chest x-ray imaging.Scientific reports · 2025Article
- Designing the CORI score for COVID-19 diagnosis in parallel with deep learning-based imaging models.Narra J · 2025Article
- Deep learning-based cough classification using application-recorded sounds: a transfer learning approach with VGGish.BMC medical informatics and decision making · 2025Article
- Breaking barriers: noninvasive AI model for BRAFInternational journal of computer assisted radiology and surgery · 2025Article
- RETRACTED: Recognition method of bridge apparent defects based on image processing and improved convolutional neural networks.PloS one · 2025Article
- Image-based deep learning in diagnosing mycoplasma pneumonia on pediatric chest X-rays.BMC pediatrics · 2024Article
- Lung Disease Detection Using U-Net Feature Extractor Cascaded by Graph Convolutional Network.Diagnostics (Basel, Switzerland) · 2024Article
- In Vivo Prediction of Breast Muscle Weight in Broiler Chickens Using X-ray Images Based on Deep Learning and Machine Learning.Animals : an open access journal from MDPI · 2024Article
- Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data.Journal of personalized medicine · 2023Review
- Parallel CNN-ELM: A multiclass classification of chest X-ray images to identify seventeen lung diseases including COVID-19.Expert systems with applications · 2023Article
- A lightweight CORONA-NET for COVID-19 detection in X-ray images.Expert systems with applications · 2023Article
- Telemedicine in Primary Practice in the Age of the COVID-19 Pandemic-Review.Medicina (Kaunas, Lithuania) · 2023Review
- Optical Biosensors for the Diagnosis of COVID-19 and Other Viruses-A Review.Diagnostics (Basel, Switzerland) · 2023Review
53 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coronavirus is still the leading cause of death worldwide. There are a set number of COVID-19 test units accessible in emergency clinics because of the expanding cases daily. Therefore, it is important to implement an automatic detection and classification system as a speedy elective finding choice to forestall COVID-19 spreading among individuals. Medical images analysis is one of the most promising research areas, it provides facilities for diagnosis and making decisions of a number of diseases such as Coronavirus. This paper conducts a comparative study of the use of the recent deep learning models (VGG16, VGG19, DenseNet201, Inception_ResNet_V2, Inception_V3, Resnet50, and MobileNet_V2) to deal with detection and classification of coronavirus pneumonia. The experiments were conducted using chest X-ray & CT dataset of 6087 images (2780 images of bacterial pneumonia, 1493 of coronavirus, 231 of Covid19, and 1583 normal) and confusion matrices are used to evaluate model performances. Results found out that the use of inception_Resnet_V2 and Densnet201 provide better results compared to other models used in this work (92.18% accuracy for Inception-ResNetV2 and 88.09% accuracy for Densnet201).Communicated by Ramaswamy H. Sarma.
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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.