Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset
The ongoing COVID-19 pandemic has caused devastating effects on humanity worldwide. With practical advantages and wide accessibility, chest X-rays (CXRs) play vital roles in the diagnosis of COVID-19 and the evaluation of the extent of lung damages incurred by the virus. This study aimed to leverage...
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MDPI AG
2021-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/22/10528 |
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| author | Khin Yadanar Win Noppadol Maneerat Syna Sreng Kazuhiko Hamamoto |
| author_facet | Khin Yadanar Win Noppadol Maneerat Syna Sreng Kazuhiko Hamamoto |
| author_sort | Khin Yadanar Win |
| collection | DOAJ |
| description | The ongoing COVID-19 pandemic has caused devastating effects on humanity worldwide. With practical advantages and wide accessibility, chest X-rays (CXRs) play vital roles in the diagnosis of COVID-19 and the evaluation of the extent of lung damages incurred by the virus. This study aimed to leverage deep-learning-based methods toward the automated classification of COVID-19 from normal and viral pneumonia on CXRs, and the identification of indicative regions of COVID-19 biomarkers. Initially, we preprocessed and segmented the lung regions usingDeepLabV3+ method, and subsequently cropped the lung regions. The cropped lung regions were used as inputs to several deep convolutional neural networks (CNNs) for the prediction of COVID-19. The dataset was highly unbalanced; the vast majority were normal images, with a small number of COVID-19 and pneumonia images. To remedy the unbalanced distribution and to avoid biased classification results, we applied five different approaches: (i) balancing the class using weighted loss; (ii) image augmentation to add more images to minority cases; (iii) the undersampling of majority classes; (iv) the oversampling of minority classes; and (v) a hybrid resampling approach of oversampling and undersampling. The best-performing methods from each approach were combined as the ensemble classifier using two voting strategies. Finally, we used the saliency map of CNNs to identify the indicative regions of COVID-19 biomarkers which are deemed useful for interpretability. The algorithms were evaluated using the largest publicly available COVID-19 dataset. An ensemble of the top five CNNs with image augmentation achieved the highest accuracy of 99.23% and area under curve (AUC) of 99.97%, surpassing the results of previous studies. |
| first_indexed | 2024-03-10T05:44:02Z |
| format | Article |
| id | doaj.art-4cb34389d3d5420d848a1865b585639c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T05:44:02Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-4cb34389d3d5420d848a1865b585639c2023-11-22T22:15:05ZengMDPI AGApplied Sciences2076-34172021-11-0111221052810.3390/app112210528Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray DatasetKhin Yadanar Win0Noppadol Maneerat1Syna Sreng2Kazuhiko Hamamoto3King Mongkut’s Institute of Technology Ladkrabang, School of Engineering, Bangkok 10520, ThailandKing Mongkut’s Institute of Technology Ladkrabang, School of Engineering, Bangkok 10520, ThailandKing Mongkut’s Institute of Technology Ladkrabang, School of Engineering, Bangkok 10520, ThailandSchool of Information and Telecommunication Engineering, Tokai University, Tokyo 108-8619, JapanThe ongoing COVID-19 pandemic has caused devastating effects on humanity worldwide. With practical advantages and wide accessibility, chest X-rays (CXRs) play vital roles in the diagnosis of COVID-19 and the evaluation of the extent of lung damages incurred by the virus. This study aimed to leverage deep-learning-based methods toward the automated classification of COVID-19 from normal and viral pneumonia on CXRs, and the identification of indicative regions of COVID-19 biomarkers. Initially, we preprocessed and segmented the lung regions usingDeepLabV3+ method, and subsequently cropped the lung regions. The cropped lung regions were used as inputs to several deep convolutional neural networks (CNNs) for the prediction of COVID-19. The dataset was highly unbalanced; the vast majority were normal images, with a small number of COVID-19 and pneumonia images. To remedy the unbalanced distribution and to avoid biased classification results, we applied five different approaches: (i) balancing the class using weighted loss; (ii) image augmentation to add more images to minority cases; (iii) the undersampling of majority classes; (iv) the oversampling of minority classes; and (v) a hybrid resampling approach of oversampling and undersampling. The best-performing methods from each approach were combined as the ensemble classifier using two voting strategies. Finally, we used the saliency map of CNNs to identify the indicative regions of COVID-19 biomarkers which are deemed useful for interpretability. The algorithms were evaluated using the largest publicly available COVID-19 dataset. An ensemble of the top five CNNs with image augmentation achieved the highest accuracy of 99.23% and area under curve (AUC) of 99.97%, surpassing the results of previous studies.https://www.mdpi.com/2076-3417/11/22/10528COVID-19chest X-raysdeep learningensemble learningimage augmentationoversampling |
| spellingShingle | Khin Yadanar Win Noppadol Maneerat Syna Sreng Kazuhiko Hamamoto Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset Applied Sciences COVID-19 chest X-rays deep learning ensemble learning image augmentation oversampling |
| title | Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset |
| title_full | Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset |
| title_fullStr | Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset |
| title_full_unstemmed | Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset |
| title_short | Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset |
| title_sort | ensemble deep learning for the detection of covid 19 in unbalanced chest x ray dataset |
| topic | COVID-19 chest X-rays deep learning ensemble learning image augmentation oversampling |
| url | https://www.mdpi.com/2076-3417/11/22/10528 |
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