ArticleAn international journal on information fusion2021
COVID-19 classification by CCSHNet with deep fusion using transfer learning and discriminant correlation analysis.
Article in An international journal on information fusion, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 2 of them syntheses that pooled it.
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Who cites it
65 citing papers in PubMed, 2 syntheses or guidelines pooled it, 240 citations in OpenAlex.
- Automatic Classification of Emotions Based on Cardiac Signals: A Systematic Literature Review.Annals of biomedical engineering · 2023Pooled it
- The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions.Computers in biology and medicine · 2022Pooled it
- Performance of AI Approaches for COVID-19 Diagnosis Using Chest CT Scans: The Impact of Architecture and Dataset.RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin · 2026Article
- GM-CBAM-ResNet: A Lightweight Deep Learning Network for Diagnosis of COVID-19.Journal of imaging · 2025Article
- Article
- Heartbeat classification method combining multi-branch convolutional neural networks and transformer.iScience · 2024Article
- Imaging features and deep learning for prediction of pulmonary epithelioid hemangioendothelioma in CT images.Journal of thoracic disease · 2024Article
- Pashto script and graphics detection in camera captured Pashto document images using deep learning model.PeerJ. Computer science · 2024Article
- Emotion detection from handwriting and drawing samples using an attention-based transformer model.PeerJ. Computer science · 2024Article
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
- MTMC-AUR2CNet: Multi-textural multi-class attention recurrent residual convolutional neural network for COVID-19 classification using chest X-ray images.Biomedical signal processing and control · 2023Article
- PneuNet: deep learning for COVID-19 pneumonia diagnosis on chest X-ray image analysis using Vision Transformer.Medical & biological engineering & computing · 2023Article
- Simultaneous Super-Resolution and Classification of Lung Disease Scans.Diagnostics (Basel, Switzerland) · 2023Article
- Automated diagnosis of COVID-19 using radiological modalities and Artificial Intelligence functionalities: A retrospective study based on chest HRCT database.Biomedical signal processing and control · 2023Article
- Deep Feature Engineering in Colposcopy Image Recognition: A Comparative Study.Bioengineering (Basel, Switzerland) · 2023Article
- Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment.Sensors (Basel, Switzerland) · 2023Review
- A multi-class classification framework for disease screening and disease diagnosis of COVID-19 from chest X-ray images.Multimedia tools and applications · 2023Article
- Diagnosis of COVID-19 with simultaneous accurate prediction of cardiac abnormalities from chest computed tomographic images.PloS one · 2023Article
- Swin-textural: A novel textural features-based image classification model for COVID-19 detection on chest computed tomography.Informatics in medicine unlocked · 2023Article
- Automatic diagnosis of COVID-19 with MCA-inspired TQWT-based classification of chest X-ray images.Computers in biology and medicine · 2023Article
5 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aim: COVID-19 is a disease caused by a new strain of coronavirus. Up to 18th October 2020, worldwide there have been 39.6 million confirmed cases resulting in more than 1.1 million deaths. To improve diagnosis, we aimed to design and develop a novel advanced AI system for COVID-19 classification based on chest CT (CCT) images.
methods: Our dataset from local hospitals consisted of 284 COVID-19 images, 281 community-acquired pneumonia images, 293 secondary pulmonary tuberculosis images; and 306 healthy control images. We first used pretrained models (PTMs) to learn features, and proposed a novel (L, 2) transfer feature learning algorithm to extract features, with a hyperparameter of number of layers to be removed (NLR, symbolized as
results: On the test set, CCSHNet achieved sensitivities of four classes of 95.61%, 96.25%, 98.30%, and 97.86%, respectively. The precision values of four classes were 97.32%, 96.42%, 96.99%, and 97.38%, respectively. The F1 scores of four classes were 96.46%, 96.33%, 97.64%, and 97.62%, respectively. The MA F1 score was 97.04%. In addition, CCSHNet outperformed 12 state-of-the-art COVID-19 detection methods.
conclusions: CCSHNet is effective in detecting COVID-19 and other lung infectious diseases using first-line clinical imaging and can therefore assist radiologists in making accurate diagnoses based on CCTs.
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Registered trials
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.