An Ensemble of Global and Local-Attention Based Convolutional Neural Networks for COVID-19 Diagnosis on Chest X-ray Images
The recent Coronavirus Disease 2019 (COVID-19) pandemic has put a tremendous burden on global health systems. Medical practitioners are under great pressure for reliable screening of suspected cases employing adjunct diagnostic tools to standard point-of-care testing methodology. Chest X-rays (CXRs)...
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MDPI AG
2021-01-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/13/1/113 |
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| author | Ahmed Afifi Noor E Hafsa Mona A. S. Ali Abdulaziz Alhumam Safa Alsalman |
| author_facet | Ahmed Afifi Noor E Hafsa Mona A. S. Ali Abdulaziz Alhumam Safa Alsalman |
| author_sort | Ahmed Afifi |
| collection | DOAJ |
| description | The recent Coronavirus Disease 2019 (COVID-19) pandemic has put a tremendous burden on global health systems. Medical practitioners are under great pressure for reliable screening of suspected cases employing adjunct diagnostic tools to standard point-of-care testing methodology. Chest X-rays (CXRs) are appearing as a prospective diagnostic tool with easy-to-acquire, low-cost and less cross-contamination risk features. Artificial intelligence (AI)-attributed CXR evaluation has shown great potential for distinguishing COVID-19-induced pneumonia from other associated clinical instances. However, one of the associated challenges with diagnostic imaging-based modeling is incorrect feature attribution, which leads the model to learn misguiding disease patterns, causing wrong predictions. Here, we demonstrate an effective deep learning-based methodology to mitigate the problem, thereby allowing the classification algorithm to learn from relevant features. The proposed deep-learning framework consists of an ensemble of convolutional neural network (CNN) models focusing on both global and local pathological features from CXR lung images, while the latter is extracted using a multi-instance learning scheme and a local attention mechanism. An inspection of a series of backbone CNN models using global and local features, and an ensemble of both features, trained from high-quality CXR images of 1311 patients, further augmented for achieving the symmetry in class distribution, to localize lung pathological features followed by the classification of COVID-19 and other related pneumonia, shows that a DenseNet161 architecture outperforms all other models, as evaluated on an independent test set of 159 patients with confirmed cases. Specifically, an ensemble of DenseNet161 models with global and local attention-based features achieve an average balanced accuracy of 91.2%, average precision of 92.4%, and F1-score of 91.9% in a multi-label classification framework comprising COVID-19, pneumonia, and control classes. The DenseNet161 ensembles were also found to be statistically significant from all other models in a comprehensive statistical analysis. The current study demonstrated that the proposed deep learning-based algorithm can accurately identify the COVID-19-related pneumonia in CXR images, along with differentiating non-COVID-19-associated pneumonia with high specificity, by effectively alleviating the incorrect feature attribution problem, and exploiting an enhanced feature descriptor. |
| first_indexed | 2024-03-09T05:13:25Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-09T05:13:25Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-c4190d7270cb444e834e8b3177d7c1c92023-12-03T12:47:46ZengMDPI AGSymmetry2073-89942021-01-0113111310.3390/sym13010113An Ensemble of Global and Local-Attention Based Convolutional Neural Networks for COVID-19 Diagnosis on Chest X-ray ImagesAhmed Afifi0Noor E Hafsa1Mona A. S. Ali2Abdulaziz Alhumam3Safa Alsalman4Department of Computer Science, College of Computer Science and Information Technology, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi ArabiaDepartment of Computer Science, College of Computer Science and Information Technology, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi ArabiaDepartment of Computer Science, College of Computer Science and Information Technology, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi ArabiaDepartment of Computer Science, College of Computer Science and Information Technology, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi ArabiaDepartment of Computer Science, College of Computer Science and Information Technology, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi ArabiaThe recent Coronavirus Disease 2019 (COVID-19) pandemic has put a tremendous burden on global health systems. Medical practitioners are under great pressure for reliable screening of suspected cases employing adjunct diagnostic tools to standard point-of-care testing methodology. Chest X-rays (CXRs) are appearing as a prospective diagnostic tool with easy-to-acquire, low-cost and less cross-contamination risk features. Artificial intelligence (AI)-attributed CXR evaluation has shown great potential for distinguishing COVID-19-induced pneumonia from other associated clinical instances. However, one of the associated challenges with diagnostic imaging-based modeling is incorrect feature attribution, which leads the model to learn misguiding disease patterns, causing wrong predictions. Here, we demonstrate an effective deep learning-based methodology to mitigate the problem, thereby allowing the classification algorithm to learn from relevant features. The proposed deep-learning framework consists of an ensemble of convolutional neural network (CNN) models focusing on both global and local pathological features from CXR lung images, while the latter is extracted using a multi-instance learning scheme and a local attention mechanism. An inspection of a series of backbone CNN models using global and local features, and an ensemble of both features, trained from high-quality CXR images of 1311 patients, further augmented for achieving the symmetry in class distribution, to localize lung pathological features followed by the classification of COVID-19 and other related pneumonia, shows that a DenseNet161 architecture outperforms all other models, as evaluated on an independent test set of 159 patients with confirmed cases. Specifically, an ensemble of DenseNet161 models with global and local attention-based features achieve an average balanced accuracy of 91.2%, average precision of 92.4%, and F1-score of 91.9% in a multi-label classification framework comprising COVID-19, pneumonia, and control classes. The DenseNet161 ensembles were also found to be statistically significant from all other models in a comprehensive statistical analysis. The current study demonstrated that the proposed deep learning-based algorithm can accurately identify the COVID-19-related pneumonia in CXR images, along with differentiating non-COVID-19-associated pneumonia with high specificity, by effectively alleviating the incorrect feature attribution problem, and exploiting an enhanced feature descriptor.https://www.mdpi.com/2073-8994/13/1/113COVID-19 detectionpneumonia diagnosisconvolutional neural networkmulti-instance learningwrong feature attributionmulti-label classification |
| spellingShingle | Ahmed Afifi Noor E Hafsa Mona A. S. Ali Abdulaziz Alhumam Safa Alsalman An Ensemble of Global and Local-Attention Based Convolutional Neural Networks for COVID-19 Diagnosis on Chest X-ray Images Symmetry COVID-19 detection pneumonia diagnosis convolutional neural network multi-instance learning wrong feature attribution multi-label classification |
| title | An Ensemble of Global and Local-Attention Based Convolutional Neural Networks for COVID-19 Diagnosis on Chest X-ray Images |
| title_full | An Ensemble of Global and Local-Attention Based Convolutional Neural Networks for COVID-19 Diagnosis on Chest X-ray Images |
| title_fullStr | An Ensemble of Global and Local-Attention Based Convolutional Neural Networks for COVID-19 Diagnosis on Chest X-ray Images |
| title_full_unstemmed | An Ensemble of Global and Local-Attention Based Convolutional Neural Networks for COVID-19 Diagnosis on Chest X-ray Images |
| title_short | An Ensemble of Global and Local-Attention Based Convolutional Neural Networks for COVID-19 Diagnosis on Chest X-ray Images |
| title_sort | ensemble of global and local attention based convolutional neural networks for covid 19 diagnosis on chest x ray images |
| topic | COVID-19 detection pneumonia diagnosis convolutional neural network multi-instance learning wrong feature attribution multi-label classification |
| url | https://www.mdpi.com/2073-8994/13/1/113 |
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