Article ; Online: A Deep Ensemble Dynamic Learning Network for Corona Virus Disease 2019 Diagnosis.
IEEE transactions on neural networks and learning systems
2024 Volume 35, Issue 3, Page(s) 3912–3926
Abstract: Corona virus disease 2019 is an extremely fatal pandemic around the world. Intelligently recognizing X-ray chest radiography images for automatically identifying corona virus disease 2019 from other types of pneumonia and normal cases provides clinicians ...
| Abstract | Corona virus disease 2019 is an extremely fatal pandemic around the world. Intelligently recognizing X-ray chest radiography images for automatically identifying corona virus disease 2019 from other types of pneumonia and normal cases provides clinicians with tremendous conveniences in diagnosis process. In this article, a deep ensemble dynamic learning network is proposed. After a chain of image preprocessing steps and the division of image dataset, convolution blocks and the final average pooling layer are pretrained as a feature extractor. For classifying the extracted feature samples, two-stage bagging dynamic learning network is trained based on neural dynamic learning and bagging algorithms, which diagnoses the presence and types of pneumonia successively. Experimental results manifest that using the proposed deep ensemble dynamic learning network obtains 98.7179% diagnosis accuracy, which indicates more excellent diagnosis effect than existing state-of-the-art models on the open image dataset. Such accurate diagnosis effects provide convincing evidences for further detections and treatments. |
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| MeSH term(s) | Humans ; COVID-19/diagnostic imaging ; Neural Networks, Computer ; Algorithms ; Learning ; COVID-19 Testing |
| Language | English |
| Publishing date | 2024-02-29 |
| Publishing country | United States |
| Document type | Journal Article |
| ISSN | 2162-2388 |
| ISSN (online) | 2162-2388 |
| DOI | 10.1109/TNNLS.2022.3201198 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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