ArticleExpert systems with applications2021
A deep-learning-based framework for severity assessment of COVID-19 with CT images.
Article in Expert systems with applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Test Accuracy of Deep Learning Prediction Models on COVID-19 Severity: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2023Pooled it
- Classification of the ICU Admission for COVID-19 Patients with Transfer Learning Models Using Chest X-Ray Images.Diagnostics (Basel, Switzerland) · 2025Article
- A hybrid inception-dilated-ResNet architecture for deep learning-based prediction of COVID-19 severity.Scientific reports · 2025Article
- Research on the selection of OTC drug supply chain sales models: based on medical insurance policy.Frontiers in public health · 2025Article
- Enhancing Medical Image Classification through Transfer Learning and CLAHE Optimization.Current medical imaging · 2025Article
- Efficient management of pulmonary embolism diagnosis using a two-step interconnected machine learning model based on electronic health records data.Health information science and systems · 2024Article
- Identifying severe community-acquired pneumonia using radiomics and clinical data: a machine learning approach.Scientific reports · 2024Article
- Deep Learning-Based Classification of Chest Diseases Using X-rays, CT Scans, and Cough Sound Images.Diagnostics (Basel, Switzerland) · 2023Article
- Co-ERA-Net: Co-Supervision and Enhanced Region Attention for Accurate Segmentation in COVID-19 Chest Infection Images.Bioengineering (Basel, Switzerland) · 2023Article
- Deep learning for deterioration prediction of COVID-19 patients based on time-series of three vital signs.Scientific reports · 2023Article
- Self-paced Multi-view Learning for CT-based severity assessment of COVID-19.Biomedical signal processing and control · 2023Article
- Fruit-CoV: An efficient vision-based framework for speedy detection and diagnosis of SARS-CoV-2 infections through recorded cough sounds.Expert systems with applications · 2023Article
- Blockchain-Federated and Deep-Learning-Based Ensembling of Capsule Network with Incremental Extreme Learning Machines for Classification of COVID-19 Using CT Scans.Bioengineering (Basel, Switzerland) · 2023Article
- Weakly supervised segmentation of COVID-19 infection with local lesion coherence on CT images.Biomedical signal processing and control · 2023Article
- 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
- Construction of a predictive model for radiation proctitis after radiotherapy for female pelvic tumors based on machine learning.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2022Article
- CT-based severity assessment for COVID-19 using weakly supervised non-local CNN.Applied soft computing · 2022Article
- Detection and Prevention of Virus Infection.Advances in experimental medicine and biology · 2022Article
- Automatic Diagnosis of Stage of COVID-19 Patients using an Ensemble of Transfer Learning with Convolutional Neural Networks Based on Computed Tomography Images.Journal of medical signals and sensorsArticle
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Millions of positive COVID-19 patients are suffering from the pandemic around the world, a critical step in the management and treatment is severity assessment, which is quite challenging with the limited medical resources. Currently, several artificial intelligence systems have been developed for the severity assessment. However, imprecise severity assessment and insufficient data are still obstacles. To address these issues, we proposed a novel deep-learning-based framework for the fine-grained severity assessment using 3D CT scans, by jointly performing lung segmentation and lesion segmentation. The main innovations in the proposed framework include: 1) decomposing 3D CT scan into multi-view slices for reducing the complexity of 3D model, 2) integrating prior knowledge (dual-Siamese channels and clinical metadata) into our model for improving the model performance. We evaluated the proposed method on 1301 CT scans of 449 COVID-19 cases collected by us, our method achieved an accuracy of 86.7% for four-way classification, with the sensitivities of 92%, 78%, 95%, 89% for four stages. Moreover, ablation study demonstrated the effectiveness of the major components in our model. This indicates that our method may contribute a potential solution to severity assessment of COVID-19 patients using CT images and clinical metadata.
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