ArticleJournal of healthcare engineering2022
Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models.
Article in Journal of healthcare engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 15 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 38 citations in OpenAlex.
- Classification of Severity of Lung Parenchyma Using Saliency and Discrete Cosine Transform Energy in Computed Tomography of Patients With COVID-19.International journal of telemedicine and applications · 2025Article
- Revolutionizing healthcare: a comparative insight into deep learning's role in medical imaging.Scientific reports · 2024Article
- COVID-19 infection segmentation using hybrid deep learning and image processing techniques.Scientific reports · 2023Article
- A general approach for automatic segmentation of pneumonia, pulmonary nodule, and tuberculosis in CT images.iScience · 2023Article
- COVID-19 CT ground-glass opacity segmentation based on attention mechanism threshold.Biomedical signal processing and control · 2023Article
- Classification of COVID-19 from community-acquired pneumonia: Boosting the performance with capsule network and maximum intensity projection image of CT scans.Computers in biology and medicine · 2023Article
- Rise of Deep Learning Clinical Applications and Challenges in Omics Data: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.Computational intelligence and neuroscience · 2023Article
- Retracted: Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models.Journal of healthcare engineering · 2023Article
- EVAE-Net: An Ensemble Variational Autoencoder Deep Learning Network for COVID-19 Classification Based on Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2022Article
- Automated system for classification of COVID-19 infection from lung CT images based on machine learning and deep learning techniques.Scientific reports · 2022Article
- A novel deep fusion strategy for COVID-19 prediction using multimodality approach.Computers & electrical engineering : an international journal · 2022Article
- Novel Crow Swarm Optimization Algorithm and Selection Approach for Optimal Deep Learning COVID-19 Diagnostic Model.Computational intelligence and neuroscience · 2022Article
- COVID-RDNet: A novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images.Biocybernetics and biomedical engineeringArticle
- SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19.Biocybernetics and biomedical engineeringArticle
Corrections and comments
- Retraction · 2023-10-11Computer-Aided Content or Computer-Generated Content · Concerns/Issues about Data · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
8 authors at 7 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coronavirus disease 2019 (COVID-19) is a novel disease that affects healthcare on a global scale and cannot be ignored because of its high fatality rate. Computed tomography (CT) images are presently being employed to assist doctors in detecting COVID-19 in its early stages. In several scenarios, a combination of epidemiological criteria (contact during the incubation period), the existence of clinical symptoms, laboratory tests (nucleic acid amplification tests), and clinical imaging-based tests are used to diagnose COVID-19. This method can miss patients and cause more complications. Deep learning is one of the techniques that has been proven to be prominent and reliable in several diagnostic domains involving medical imaging. This study utilizes a convolutional neural network (CNN), stacked autoencoder, and deep neural network to develop a COVID-19 diagnostic system. In this system, classification undergoes some modification before applying the three CT image techniques to determine normal and COVID-19 cases. A large-scale and challenging CT image dataset was used in the training process of the employed deep learning model and reporting their final performance. Experimental outcomes show that the highest accuracy rate was achieved using the CNN model with an accuracy of 88.30%, a sensitivity of 87.65%, and a specificity of 87.97%. Furthermore, the proposed system has outperformed the current existing state-of-the-art models in detecting the COVID-19 virus using CT images.
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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.