ArticleScientific reports2021
Deep learning for COVID-19 detection based on CT images.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
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Who cites it
50 citing papers in PubMed.
- Automatic Body Region Classification in CT Scans Using Deep Learning.Journal of imaging informatics in medicine · 2026Article
- Review
- Diagnostics of diabetic retinopathy based on fundus photos using machine learning methods with advanced feature engineering algorithms.Scientific reports · 2025Article
- RETRACTED: LGD_Net: Capsule network with extreme learning machine for classification of lung diseases using CT scans.PloS one · 2025Article
- Deep learning model to diagnose cardiac amyloidosis from haematoxylin/eosin-stained myocardial tissue.European heart journal. Imaging methods and practice · 2025Article
- Predicting SARS-CoV-2 infection among hemodialysis patients using deep neural network methods.Scientific reports · 2024Article
- Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- RETRACTED: Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds.PloS one · 2024Article
- TransformEHR: transformer-based encoder-decoder generative model to enhance prediction of disease outcomes using electronic health records.Nature communications · 2023Article
- Detection and characterization of spike architecture based on deep learning and X-ray computed tomography in barley.Plant methods · 2023Article
- Challenges of AI driven diagnosis of chest X-rays transmitted through smart phones: a case study in COVID-19.Scientific reports · 2023Article
- COVID-Net Biochem: an explainability-driven framework to building machine learning models for predicting survival and kidney injury of COVID-19 patients from clinical and biochemistry data.Scientific reports · 2023Article
- Biased Deep Learning Methods in Detection of COVID-19 Using CT Images: A Challenge Mounted by Subject-Wise-Split ISFCT Dataset.Journal of imaging · 2023Article
- Automated COVID-19 detection with convolutional neural networks.Scientific reports · 2023Article
- A deep learning predictive model for public health concerns and hesitancy toward the COVID-19 vaccines.Scientific reports · 2023Article
- Learning without forgetting by leveraging transfer learning for detecting COVID-19 infection from CT images.Scientific reports · 2023Article
- COVID-19 diagnosis utilizing wavelet-based contrastive learning with chest CT images.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2023Article
- Deep Learning Methods for Interpretation of Pulmonary CT and X-ray Images in Patients with COVID-19-Related Lung Involvement: A Systematic Review.Journal of clinical medicine · 2023Review
- COVID-19 Diagnosis in Computerized Tomography (CT) and X-ray Scans Using Capsule Neural Network.Diagnostics (Basel, Switzerland) · 2023Article
- Cross-platform privacy-preserving CT image COVID-19 diagnosis based on source-free domain adaptation.Knowledge-based systems · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
COVID-19 has tremendously impacted patients and medical systems globally. Computed tomography images can effectively complement the reverse transcription-polymerase chain reaction testing. This study adopted a convolutional neural network for COVID-19 testing. We examined the performance of different pre-trained models on CT testing and identified that larger, out-of-field datasets boost the testing power of the models. This suggests that a priori knowledge of the models from out-of-field training is also applicable to CT images. The proposed transfer learning approach proves to be more successful than the current approaches described in literature. We believe that our approach has achieved the state-of-the-art performance in identification thus far. Based on experiments with randomly sampled training datasets, the results reveal a satisfactory performance by our model. We investigated the relevant visual characteristics of the CT images used by the model; these may assist clinical doctors in manual screening.
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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.