Stacking Ensemble and ECA-EfficientNetV2 Convolutional Neural Networks on Classification of Multiple Chest Diseases Including COVID-19.

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Mei-Ling Huang, Yu-Chieh Liao

Author Information

Mei-Ling Huang: Department of Industrial Engineering & Management, National Chin-Yi University of Technology, 57, Sec. 2, Zhongshan Rd., Taiping Dist., Taichung, 411030, Taiwan. Electronic address: huangml@ncut.edu.tw.
Yu-Chieh Liao: Department of Industrial Engineering & Management, National Chin-Yi University of Technology, 57, Sec. 2, Zhongshan Rd., Taiping Dist., Taichung, 411030, Taiwan.

PMID: 36526533 DOI: 10.1016/j.acra.2022.11.027

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.

COVID-19 Convolutional neural network Ensemble learning Stacking

Humans

COVID-19

COVID-19 Testing

Thorax

Benchmarking

Neural Networks, Computer

Journal Article

Efficient and visualizable convolutional neural networks for COVID-19 classification using Chest CT.XCOVNet: Chest X-ray Image Classification for COVID-19 Early Detection Using Convolutional Neural Networks.ChestX-Ray6: Prediction of multiple diseases including COVID-19 from chest X-ray images using convolutional neural network.Lung Sound Classification Using Snapshot Ensemble of Convolutional Neural Networks.A comprehensive study on classification of COVID-19 on computed tomography with pretrained convolutional neural networks.Microscopic segmentation and classification of COVID-19 infection with ensemble convolutional neural network.PulDi-COVID: Chronic obstructive pulmonary (lung) diseases with COVID-19 classification using ensemble deep convolutional neural network from chest X-ray images to minimize severity and mortality rates.ETCNN: Extra Tree and Convolutional Neural Network-based Ensemble Model for COVID-19 Tweets Sentiment Classification.Discovery of a Generalization Gap of Convolutional Neural Networks on COVID-19 X-Rays Classification.A Fine-tuned deep convolutional neural network for chest radiography image classification on COVID-19 cases.

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