ArticleInternational journal of environmental research and public health2021
Role of Hybrid Deep Neural Networks (HDNNs), Computed Tomography, and Chest X-rays for the Detection of COVID-19.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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35 citing papers in PubMed, 100 citations in OpenAlex.
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- DECNet: Left Atrial Pulmonary Vein Class Imbalance Classification Network.Journal of imaging informatics in medicine · 2025Article
- COVID-ConvNet: A Convolutional Neural Network Classifier for Diagnosing COVID-19 Infection.Diagnostics (Basel, Switzerland) · 2023Article
- Investigating the performance of machine learning algorithms in predicting the survival of COVID-19 patients: A cross section study of Iran.Health science reports · 2023Article
- Fitness dependent optimizer with neural networks for COVID-19 patients.Computer methods and programs in biomedicine update · 2023Article
- RETRACTED ARTICLE: Covid-19 classification using sigmoid based hyper-parameter modified DNN for CT scans and chest X-rays.Multimedia tools and applications · 2023Article
- Multi-Stage Temporal Convolutional Network with Moment Loss and Positional Encoding for Surgical Phase Recognition.Diagnostics (Basel, Switzerland) · 2022Article
- COVID-19 detection based on pre-trained deep networks and LSTM model using X-ray images enhanced contrast with artificial bee colony algorithm.Expert systems · 2022Article
- E-GCS: Detection of COVID-19 through classification by attention bottleneck residual network.Engineering applications of artificial intelligence · 2022Article
- Computer Aided COVID-19 Diagnosis in Pandemic Era Using CNN in Chest X-ray Images.Life (Basel, Switzerland) · 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 Turf-Based Feature Selection Technique for Predicting Factors Affecting Human Health during Pandemic.Life (Basel, Switzerland) · 2022Article
- Combining the advantages of AlexNet convolutional deep neural network optimized with anopheles search algorithm based feature extraction and random forest classifier for COVID-19 classification.Concurrency and computation : practice & experience · 2022Article
- Review of COVID-19 testing and diagnostic methods.Talanta · 2022Review
- COVID-19 image classification using deep learning: Advances, challenges and opportunities.Computers in biology and medicine · 2022Review
- Automated COVID-19 Classification Using Heap-Based Optimization with the Deep Transfer Learning Model.Computational intelligence and neuroscience · 2022Article
- Artificial Intelligence and Deep Learning Assisted Rapid Diagnosis of COVID-19 from Chest Radiographical Images: A Survey.Contrast media & molecular imaging · 2022Review
- Multiclass Classification for Detection of COVID-19 Infection in Chest X-Rays Using CNN.Computational intelligence and neuroscience · 2022Article
- Calibrated bagging deep learning for image semantic segmentation: A case study on COVID-19 chest X-ray image.PloS one · 2022Article
Corrections and comments
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Authors and funding
11 authors at 7 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 syndrome has extensively escalated worldwide with the induction of the year 2020 and has resulted in the illness of millions of people. COVID-19 patients bear an elevated risk once the symptoms deteriorate. Hence, early recognition of diseased patients can facilitate early intervention and avoid disease succession. This article intends to develop a hybrid deep neural networks (HDNNs), using computed tomography (CT) and X-ray imaging, to predict the risk of the onset of disease in patients suffering from COVID-19. To be precise, the subjects were classified into 3 categories namely normal, Pneumonia, and COVID-19. Initially, the CT and chest X-ray images, denoted as 'hybrid images' (with resolution 1080 × 1080) were collected from different sources, including GitHub, COVID-19 radiography database, Kaggle, COVID-19 image data collection, and Actual Med COVID-19 Chest X-ray Dataset, which are open source and publicly available data repositories. The 80% hybrid images were used to train the hybrid deep neural network model and the remaining 20% were used for the testing purpose. The capability and prediction accuracy of the HDNNs were calculated using the confusion matrix. The hybrid deep neural network showed a 99% classification accuracy on the test set data.
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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.