ArticlePattern recognition letters2022
Application of CycleGAN and transfer learning techniques for automated detection of COVID-19 using X-ray images.
Article in Pattern recognition letters, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 68 citations in OpenAlex.
- A systematic review and knowledge mapping on ICT-based remote and automatic COVID-19 patient monitoring and care.BMC health services research · 2023Pooled it
- Enhancing Radiographic Diagnosis: CycleGAN-Based Methods for Reducing Cast Shadow Artifacts in Wrist Radiographs.Journal of imaging informatics in medicine · 2025Article
- Segmentation-Assisted Fusion-Based Classification for Automated CXR Image Analysis.Sensors (Basel, Switzerland) · 2025Article
- Corticosteroid treatment prediction using chest X-ray and clinical data.Computational and structural biotechnology journal · 2024Article
- Auto-detection of the coronavirus disease by using deep convolutional neural networks and X-ray photographs.Scientific reports · 2024Article
- Automatic diagnosis of COVID-19 from CT images using CycleGAN and transfer learning.Applied soft computing · 2023Article
- Primate brain pattern-based automated Alzheimer's disease detection model using EEG signals.Cognitive neurodynamics · 2023Article
- AI-based wavelet and stacked deep learning architecture for detecting coronavirus (COVID-19) from chest X-ray images.Computers & electrical engineering : an international journal · 2023Article
- Deep learning models-based CT-scan image classification for automated screening of COVID-19.Biomedical signal processing and control · 2023Article
- Survival and grade of the glioma prediction using transfer learning.PeerJ. Computer science · 2023Article
- Influence of augmentation on the performance of the double ResNet-based model for chest X-ray classification.Polish journal of radiology · 2023Article
- A MobileNet-based CNN model with a novel fine-tuning mechanism for COVID-19 infection detection.Soft computing · 2023Article
- Handling class imbalance in COVID-19 chest X-ray images classification: Using SMOTE and weighted loss.Applied soft computing · 2022Article
- LCSB-inception: Reliable and effective light-chroma separated branches for Covid-19 detection from chest X-ray images.Computers in biology and medicine · 2022Article
- RESCOVIDTCNnet: A residual neural network-based framework for COVID-19 detection using TCN and EWT with chest X-ray images.Expert systems with applications · 2022Article
- A Novel Method for COVID-19 Detection Based on DCNNs and Hierarchical Structure.Computational and mathematical methods in medicine · 2022Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coronavirus (which is also known as COVID-19) is severely impacting the wellness and lives of many across the globe. There are several methods currently to detect and monitor the progress of the disease such as radiological image from patients' chests, measuring the symptoms and applying polymerase chain reaction (RT-PCR) test. X-ray imaging is one of the popular techniques used to visualise the impact of the virus on the lungs. Although manual detection of this disease using radiology images is more popular, it can be time-consuming, and is prone to human errors. Hence, automated detection of lung pathologies due to COVID-19 utilising deep learning (Bowles et al.) techniques can assist with yielding accurate results for huge databases. Large volumes of data are needed to achieve generalizable DL models; however, there are very few public databases available for detecting COVID-19 disease pathologies automatically. Standard data augmentation method can be used to enhance the models' generalizability. In this research, the Extensive COVID-19 X-ray and CT Chest Images Dataset has been used and generative adversarial network (GAN) coupled with trained, semi-supervised CycleGAN (SSA- CycleGAN) has been applied to augment the training dataset. Then a newly designed and finetuned Inception V3 transfer learning model has been developed to train the algorithm for detecting COVID-19 pandemic. The obtained results from the proposed Inception-CycleGAN model indicated Accuracy = 94.2%, Area under Curve = 92.2%, Mean Squared Error = 0.27, Mean Absolute Error = 0.16. The developed Inception-CycleGAN framework is ready to be tested with further COVID-19 X-Ray images of the chest.
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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.