Article ; Online: Stacking Ensemble and ECA-EfficientNetV2 Convolutional Neural Networks on Classification of Multiple Chest Diseases Including COVID-19.
2022 Volume 30, Issue 9, Page(s) 1915–1935
Abstract: Rationale and objectives: Early detection and treatment of COVID-19 patients is crucial. Convolutional neural networks have been proven to accurately extract features in medical images, which accelerates time required for testing and increases the ... ...
| Abstract | Rationale and objectives: Early detection and treatment of COVID-19 patients is crucial. Convolutional neural networks have been proven to accurately extract features in medical images, which accelerates time required for testing and increases the effectiveness of COVID-19 diagnosis. This study proposes two classification models for multiple chest diseases including COVID-19. Materials and methods: The first is Stacking-ensemble model, which stacks six pretrained models including EfficientNetV2-B0, EfficientNetV2-B1, EfficientNetV2-B2, EfficientNetV2-B3, EfficientNetV2-S and EfficientNetV2-M. The second model is self-designed model ECA-EfficientNetV2 based on ECA-Net and EfficientNetV2. Ten-fold cross validation was performed for each model on chest X-ray and CT images. One more dataset, COVID-CT dataset, was tested to verify the performance of the proposed Stacking-ensemble and ECA-EfficientNetV2 models. Results: The best performance comes from the proposed ECA-EfficientNetV2 model with the highest Accuracy of 99.21%, Precision of 99.23%, Recall of 99.25%, F1-score of 99.20%, and (area under the curve) AUC of 99.51% on chest X-ray dataset; the best performance comes from the proposed ECA-EfficientNetV2 model with the highest Accuracy of 99.81%, Precision of 99.80%, Recall of 99.80%, F1-score of 99.81%, and AUC of 99.87% on chest CT dataset. The differences for five metrics between Stacking-ensemble and ECA-EfficientNetV2 models are not significant. Conclusion: Ensemble model achieves better performance than single pretrained models. Compared to the SOTA, Stacking-ensemble and ECA-EfficientNetV2 models proposed in this study demonstrate promising performance on classification of multiple chest diseases including COVID-19. |
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| MeSH term(s) | Humans ; COVID-19/diagnostic imaging ; COVID-19 Testing ; Thorax ; Benchmarking ; Neural Networks, Computer |
| Language | English |
| Publishing date | 2022-11-25 |
| Publishing country | United States |
| Document type | Journal Article |
| ZDB-ID | 1355509-1 |
| ISSN | 1878-4046 ; 1076-6332 |
| ISSN (online) | 1878-4046 |
| ISSN | 1076-6332 |
| DOI | 10.1016/j.acra.2022.11.027 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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