ArticleMultimedia tools and applications2022
ET-NET: an ensemble of transfer learning models for prediction of COVID-19 infection through chest CT-scan images.
Article in Multimedia tools and applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed, 83 citations in OpenAlex.
- A lightweight CNN for enhanced non-small cell lung cancer classification using CT scan image.Scientific reports · 2026Article
- High dimensional predictions of suicide risk in 4.2 million US Veterans using ensemble transfer learning.Scientific reports · 2024Article
- Cloud-Based Quad Deep Ensemble Framework for the Detection of COVID-19 Omicron and Delta Variants.Diagnostics (Basel, Switzerland) · 2023Article
- How intra-source imbalanced datasets impact the performance of deep learning for COVID-19 diagnosis using chest X-ray images.Scientific reports · 2023Article
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
- Analysis of Conocurvone, Ganoderic acid A and Oleuropein molecules against the main protease molecule of COVID-19 byEngineering analysis with boundary elements · 2023Article
- RADIC:A tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2023Article
- Predicting the Severity of COVID-19 from Lung CT Images Using Novel Deep Learning.Journal of medical and biological engineering · 2023Article
- Texture classification for visual data using transfer learning.Multimedia tools and applications · 2022Article
- COVID-DSNet: A novel deep convolutional neural network for detection of coronavirus (SARS-CoV-2) cases from CT and Chest X-Ray images.Artificial intelligence in medicine · 2022Article
- Detection of COVID-19 and its pulmonary stage using Bayesian hyperparameter optimization and deep feature selection methods.Expert systems · 2022Article
- COVID-19 chest X-ray detection through blending ensemble of CNN snapshots.Biomedical signal processing and control · 2022Article
- Comparison of Convolutional Neural Networks and Transformers for the Classification of Images of COVID-19, Pneumonia and Healthy Individuals as Observed with Computed Tomography.Journal of imaging · 2022Article
- COVID-19 detection from CT scans using a two-stage framework.Expert systems with applications · 2022Article
- Transfer learning based novel ensemble classifier for COVID-19 detection from chest CT-scans.Computers in biology and medicine · 2022Article
- COFE-Net: An ensemble strategy for Computer-Aided Detection for COVID-19.Measurement : journal of the International Measurement Confederation · 2022Article
- Detecting COVID-19 infection status from chest X-ray and CT scanFrontiers in genetics · 2022Article
- Improved COVID-19 detection with chest x-ray images using deep learning.Multimedia tools and applications · 2022Article
- Deep autoencoder based domain adaptation for transfer learning.Multimedia tools and applications · 2022Article
- COVID-19 detection from lung CT-Scans using a fuzzy integral-based CNN ensemble.Computers in biology and medicine · 2021Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The COVID-19 virus has caused a worldwide pandemic, affecting numerous individuals and accounting for more than a million deaths. The countries of the world had to declare complete lockdown when the coronavirus led to community spread. Although the real-time Polymerase Chain Reaction (RT-PCR) test is the gold-standard test for COVID-19 screening, it is not satisfactorily accurate and sensitive. On the other hand, Computer Tomography (CT) scan images are much more sensitive and can be suitable for COVID-19 detection. To this end, in this paper, we develop a fully automated method for fast COVID-19 screening by using chest CT-scan images employing Deep Learning techniques. For this supervised image classification problem, a bootstrap aggregating or Bagging ensemble of three transfer learning models, namely, Inception v3, ResNet34 and DenseNet201, has been used to boost the performance of the individual models. The proposed framework, called ET-NET, has been evaluated on a publicly available dataset, achieving
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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.