ArticleApplied intelligence (Dordrecht, Netherlands)2022
Automatic detection of COVID-19 from chest CT scan and chest X-Rays images using deep learning, transfer learning and stacking.
Article in Applied intelligence (Dordrecht, Netherlands), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- 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
- RETRACTED: LGD_Net: Capsule network with extreme learning machine for classification of lung diseases using CT scans.PloS one · 2025Article
- Enhancing COVID-19 classification of X-ray images with hybrid deep transfer learning models.Frontiers in artificial intelligence · 2025Article
- Contribution to pulmonary diseases diagnostic from X-ray images using innovative deep learning models.Heliyon · 2024Article
- A brief review and scientometric analysis on ensemble learning methods for handling COVID-19.Heliyon · 2024Article
- COVID-19 Detection via Ultra-Low-Dose X-ray Images Enabled by Deep Learning.Bioengineering (Basel, Switzerland) · 2023Article
- The efficacy of supervised learning and semi-supervised learning in diagnosis of impacted third molar on panoramic radiographs through artificial intelligence model.Dento maxillo facial radiology · 2023Article
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
- Ensemble deep honey architecture for COVID-19 prediction using CT scan and chest X-ray images.Multimedia systems · 2023Article
- Design and Analysis of a Deep Learning Ensemble Framework Model for the Detection of COVID-19 and Pneumonia Using Large-Scale CT Scan and X-ray Image Datasets.Bioengineering (Basel, Switzerland) · 2023Article
- LDDNet: A Deep Learning Framework for the Diagnosis of Infectious Lung Diseases.Sensors (Basel, Switzerland) · 2023Article
- Automated semantic lung segmentation in chest CT images using deep neural network.Neural computing & applications · 2023Article
- A deep transfer learning-based convolution neural network model for COVID-19 detection using computed tomography scan images for medical applications.Advances in engineering software (Barking, London, England : 1992) · 2023Article
- A multi-class classification framework for disease screening and disease diagnosis of COVID-19 from chest X-ray images.Multimedia tools and applications · 2023Article
- Deep learning-based important weights-only transfer learning approach for COVID-19 CT-scan classification.Applied intelligence (Dordrecht, Netherlands) · 2023Article
- COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention.Computers in biology and medicine · 2022Article
- A Comprehensive Review of Machine Learning Used to Combat COVID-19.Diagnostics (Basel, Switzerland) · 2022Review
- 3cDe-Net: a cervical cancer cell detection network based on an improved backbone network and multiscale feature fusion.BMC medical imaging · 2022Article
- Transfer learning based novel ensemble classifier for COVID-19 detection from chest CT-scans.Computers in biology and medicine · 2022Article
- A Deep Learning and Handcrafted Based Computationally Intelligent Technique for Effective COVID-19 Detection from X-ray/CT-scan Imaging.Journal of grid computing · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One of the promising methods for early detection of Coronavirus Disease 2019 (COVID-19) among symptomatic patients is to analyze chest Computed Tomography (CT) scans or chest x-rays images of individuals using Deep Learning (DL) techniques. This paper proposes a novel stacked ensemble to detect COVID-19 either from chest CT scans or chest x-ray images of an individual. The proposed model is a stacked ensemble of heterogenous pre-trained computer vision models. Four pre-trained DL models were considered: Visual Geometry Group (VGG 19), Residual Network (ResNet 101), Densely Connected Convolutional Networks (DenseNet 169) and Wide Residual Network (WideResNet 50 2). From each pre-trained model, the potential candidates for base classifiers were obtained by varying the number of additional fully-connected layers. After an exhaustive search, three best-performing diverse models were selected to design a weighted average-based heterogeneous stacked ensemble. Five different chest CT scans and chest x-ray images were used to train and evaluate the proposed model. The performance of the proposed model was compared with two other ensemble models, baseline pre-trained computer vision models and existing models for COVID-19 detection. The proposed model achieved uniformly good performance on five different datasets, consisting of chest CT scans and chest x-rays images. In relevance to COVID-19, as the recall is more important than precision, the trade-offs between recall and precision at different thresholds were explored. Recommended threshold values which yielded a high recall and accuracy were obtained for each dataset.
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