ArticleBiocybernetics and biomedical engineering
Automated COVID-19 detection from X-ray and CT images with stacked ensemble convolutional neural network.
Article in Biocybernetics and biomedical engineering. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 66 citations in OpenAlex.
- Unlocking the Power of 3D Convolutional Neural Networks for COVID-19 Detection: A Comprehensive Review.Journal of imaging informatics in medicine · 2025Review
- Auto-detection of the coronavirus disease by using deep convolutional neural networks and X-ray photographs.Scientific reports · 2024Article
- Parallel CNN-ELM: A multiclass classification of chest X-ray images to identify seventeen lung diseases including COVID-19.Expert systems with applications · 2023Article
- Uncertainty-Aware Convolutional Neural Network for Identifying Bilateral Opacities on Chest X-rays: A Tool to Aid Diagnosis of Acute Respiratory Distress Syndrome.Bioengineering (Basel, Switzerland) · 2023Article
- Detection of various lung diseases including COVID-19 using extreme learning machine algorithm based on the features extracted from a lightweight CNN architecture.Biocybernetics and biomedical engineering · 2023Article
- Learning without forgetting by leveraging transfer learning for detecting COVID-19 infection from CT images.Scientific reports · 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
- Deep Convolutional Neural Networks for Detecting COVID-19 Using Medical Images: A Survey.New generation computing · 2023Article
- Application of Convolutional Neural Networks for COVID-19 Detection in X-ray Images Using InceptionV3 and U-Net.New generation computing · 2023Article
- Application of neighborhood-scale wastewater-based epidemiology in low COVID-19 incidence situations.The Science of the total environment · 2022Article
- TOPSIS aided ensemble of CNN models for screening COVID-19 in chest X-ray images.Scientific reports · 2022Article
- TL-med: A Two-stage transfer learning recognition model for medical images of COVID-19.Biocybernetics and biomedical engineeringArticle
- COVID-19 detection on chest X-ray images using Homomorphic Transformation and VGG inspired deep convolutional neural network.Biocybernetics and biomedical engineeringArticle
- Automated diagnosis of COVID stages from lung CT images using statistical features in 2-dimensional flexible analytic wavelet transform.Biocybernetics and biomedical engineeringArticle
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automatic and rapid screening of COVID-19 from the radiological (X-ray or CT scan) images has become an urgent need in the current pandemic situation of SARS-CoV-2 worldwide. However, accurate and reliable screening of patients is challenging due to the discrepancy between the radiological images of COVID-19 and other viral pneumonia. So, in this paper, we design a new stacked convolutional neural network model for the automatic diagnosis of COVID-19 disease from the chest X-ray and CT images. In the proposed approach, different sub-models have been obtained from the VGG19 and the Xception models during the training. Thereafter, obtained sub-models are stacked together using softmax classifier. The proposed stacked CNN model combines the discriminating power of the different CNN's sub-models and detects COVID-19 from the radiological images. In addition, we collect CT images to build a CT image dataset and also generate an X-ray images dataset by combining X-ray images from the three publicly available data repositories. The proposed stacked CNN model achieves a sensitivity of 97.62% for the multi-class classification of X-ray images into COVID-19, Normal and Pneumonia Classes and 98.31% sensitivity for binary classification of CT images into COVID-19 and no-Finding classes. Our proposed approach shows superiority over the existing methods for the detection of the COVID-19 cases from the X-ray radiological images.
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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.