ArticleApplied soft computing2023
Automatic diagnosis of COVID-19 from CT images using CycleGAN and transfer learning.
Article in Applied soft computing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 62 citations in OpenAlex.
- Image-based AI diagnostic performance for fatty liver: a systematic review and meta-analysis.BMC medical imaging · 2023Pooled it
- Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review.Computer methods and programs in biomedicine · 2022Pooled it
- Unlocking the Power of 3D Convolutional Neural Networks for COVID-19 Detection: A Comprehensive Review.Journal of imaging informatics in medicine · 2025Review
- Revolutionizing gastroenterology and hepatology with artificial intelligence: From precision diagnosis to equitable healthcare through interdisciplinary practice.World journal of gastroenterology · 2025Review
- Predictive models of severe disease in patients with COVID-19 pneumonia at an early stage on CT images using topological properties.Radiological physics and technology · 2025Article
- Exploiting adaptive neuro-fuzzy inference systems for cognitive patterns in multimodal brain signal analysis.Scientific reports · 2025Article
- A Pathological Diagnosis Method for Fever of Unknown Origin Based on Multipath Hierarchical Classification: Model Design and Validation.JMIR formative research · 2024Article
- A Real-Time Fault Diagnosis Method for Multi-Source Heterogeneous Information Fusion Based on Two-Level Transfer Learning.Entropy (Basel, Switzerland) · 2024Article
- Generative artificial intelligence to produce high-fidelity blastocyst-stage embryo images.Human reproduction (Oxford, England) · 2024Article
- A high-accuracy lightweight network model for X-ray image diagnosis: A case study of COVID detection.PloS one · 2024Article
- CCS-GAN: COVID-19 CT Scan Generation and Classification with Very Few Positive Training Images.Journal of digital imaging · 2023Article
- COVID-19 radiograph prognosis using a deep CResNeXt network.Multimedia tools and applications · 2023Article
- Analyzing Transfer Learning of Vision Transformers for Interpreting Chest Radiography.Journal of digital imaging · 2022Article
- Comprehensive Survey of Machine Learning Systems for COVID-19 Detection.Journal of imaging · 2022Review
- A deep learning based approach for automatic detection of COVID-19 cases using chest X-ray images.Biomedical signal processing and control · 2022Article
- Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review.Frontiers in molecular neuroscience · 2022Review
- Medical Images Encryption Based on Adaptive-Robust Multi-Mode Synchronization of Chen Hyper-Chaotic Systems.Sensors (Basel, Switzerland) · 2021Article
- Epileptic Seizures Detection Using Deep Learning Techniques: A Review.International journal of environmental research and public health · 2021Review
- Deep neural networks for COVID-19 detection and diagnosis using images and acoustic-based techniques: a recent review.Soft computing · 2021Article
- WOANet: Whale optimized deep neural network for the classification of COVID-19 from radiography images.Biocybernetics and biomedical engineeringArticle
Corrections and comments
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Authors and funding
9 authors at 8 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The outbreak of the corona virus disease (COVID-19) has changed the lives of most people on Earth. Given the high prevalence of this disease, its correct diagnosis in order to quarantine patients is of the utmost importance in the steps of fighting this pandemic. Among the various modalities used for diagnosis, medical imaging, especially computed tomography (CT) imaging, has been the focus of many previous studies due to its accuracy and availability. In addition, automation of diagnostic methods can be of great help to physicians. In this paper, a method based on pre-trained deep neural networks is presented, which, by taking advantage of a cyclic generative adversarial net (CycleGAN) model for data augmentation, has reached state-of-the-art performance for the task at hand, i.e., 99.60% accuracy. Also, in order to evaluate the method, a dataset containing 3163 images from 189 patients has been collected and labeled by physicians. Unlike prior datasets, normal data have been collected from people suspected of having COVID-19 disease and not from data from other diseases, and this database is made available publicly. Moreover, the method's reliability is further evaluated by calibration metrics, and its decision is interpreted by Grad-CAM also to find suspicious regions as another output of the method and make its decisions trustworthy and explainable.
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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 Socratic graph.