ArticleApplied soft computing2021
The ensemble deep learning model for novel COVID-19 on CT images.
Article in Applied soft computing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 98 papers, 2 of them syntheses that pooled it.
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Who cites it
98 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic literature review: exploring the challenges of ensemble model for medical imaging.BMC medical imaging · 2025Pooled it
- Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review.Computer methods and programs in biomedicine · 2022Pooled it
- Artificial intelligence for chest radiography: an overview of techniques, challenges, and future directions.npj health systems · 2026Review
- CELM: An Ensemble Deep Learning Model for Early Cardiomegaly Diagnosis in Chest Radiography.Diagnostics (Basel, Switzerland) · 2025Article
- Vision transformer and deep learning based weighted ensemble model for automated spine fracture type identification with GAN generated CT images.Scientific reports · 2025Article
- A cancer diagnosis transformer model based on medical IoT data for clinical measurements in predictive care systems.BioImpacts : BI · 2025Article
- Current status, challenges, and prospects of artificial intelligence applications in wound repair theranostics.Theranostics · 2025Review
- An adaptive and lightweight YOLOv5 detection model for lung tumor in PET/CT images.Scientific reports · 2024Article
- Deep-kidney: an effective deep learning framework for chronic kidney disease prediction.Health information science and systems · 2024Article
- The severity assessment and nucleic acid turning-negative-time prediction in COVID-19 patients with COPD using a fused deep learning model.BMC pulmonary medicine · 2024Article
- Reinvestigating the performance of artificial intelligence classification algorithms on COVID-19 X-Ray and CT images.iScience · 2024Article
- COVID-19 detection in lung CT slices using Brownian-butterfly-algorithm optimized lightweight deep features.Heliyon · 2024Article
- A brief review and scientometric analysis on ensemble learning methods for handling COVID-19.Heliyon · 2024Article
- A Novel Classification Model Using Optimal Long Short-Term Memory for Classification of COVID-19 from CT Images.Journal of digital imaging · 2023Article
- Deep learning based hybrid prediction model for predicting the spread of COVID-19 in the world's most populous countries.Expert systems with applications · 2023Article
- Automatic diagnosis of COVID-19 from CT images using CycleGAN and transfer learning.Applied soft computing · 2023Article
- An Efficient Ensemble Approach for Alzheimer's Disease Detection Using an Adaptive Synthetic Technique and Deep Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework.Diagnostics (Basel, Switzerland) · 2023Article
- A survey of machine learning-based methods for COVID-19 medical image analysis.Medical & biological engineering & computing · 2023Review
- Detection of Pneumonia from Chest X-ray Images Utilizing MobileNet Model.Healthcare (Basel, Switzerland) · 2023Article
38 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid detection of the novel coronavirus disease, COVID-19, has a positive effect on preventing propagation and enhancing therapeutic outcomes. This article focuses on the rapid detection of COVID-19. We propose an ensemble deep learning model for novel COVID-19 detection from CT images. 2933 lung CT images from COVID-19 patients were obtained from previous publications, authoritative media reports, and public databases. The images were preprocessed to obtain 2500 high-quality images. 2500 CT images of lung tumor and 2500 from normal lung were obtained from a hospital. Transfer learning was used to initialize model parameters and pretrain three deep convolutional neural network models: AlexNet, GoogleNet, and ResNet. These models were used for feature extraction on all images. Softmax was used as the classification algorithm of the fully connected layer. The ensemble classifier EDL-COVID was obtained via relative majority voting. Finally, the ensemble classifier was compared with three component classifiers to evaluate accuracy, sensitivity, specificity, F value, and Matthews correlation coefficient. The results showed that the overall classification performance of the ensemble model was better than that of the component classifier. The evaluation indexes were also higher. This algorithm can better meet the rapid detection requirements of the novel coronavirus disease COVID-19.
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