The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Deep Learning-Based Decision-Tree Classifier for COVID-19 Diagnosis From Chest X-ray Imaging
Open accessAbstract
The global pandemic of coronavirus disease 2019 (COVID-19) has resulted in an increased demand for testing, diagnosis, and treatment. Reverse transcription polymerase chain reaction (RT-PCR) is the definitive test for the diagnosis of COVID-19; however, chest X-ray radiography (CXR) is a fast, effective, and affordable test that identifies the possible COVID-19-related pneumonia. This study investigates the feasibility of using a deep learning-based decision-tree classifier for detecting COVID-19 from CXR images. The proposed classifier comprises three binary decision trees, each trained by a deep learning model with convolution neural network based on the PyTorch frame. The first decision tree classifies the CXR images as normal or abnormal. The second tree identifies the abnormal images that contain signs of tuberculosis, whereas the third does the same for COVID-19. The accuracies of the first and second decision trees are 98 and 80%, respectively, whereas the average accuracy of the third decision tree is 95%. The proposed deep learning-based decision-tree classifier may be used in pre-screening patients to conduct triage and fast-track decision making before RT-PCR results are available.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.369 · Cited by 4,616
CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV)
Chung, M., Bernheim, A., Mei, X., Zhang, N., Huang, M., Zeng, X., Cui, J., Xu, W., Yang, Y., Fayad, Z. A., et al. (2020) · Radiology · doi:10.1148/radiol.2020200230 · Cited by 2,751
Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification
Baltruschat, I. M., Nickisch, H., Grass, M., Knopp, T., & Saalbach, A. (2019) · Scientific Reports · doi:10.1038/s41598-019-42294-8 · Cited by 455
Melendez, J., Sánchez, C. I., Philipsen, R. H. H. M., Maduskar, P., Dawson, R., Theron, G., Dheda, K., & van Ginneken, B. (2016) · Scientific Reports · doi:10.1038/srep25265 · Cited by 152
Cited by
Fuzzy System Based Medical Image Processing for Brain Disease Prediction
Hu, M., Zhong, Y., Xie, S., Lv, H., & Lv, Z. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.714318 · Cited by 292
Explainable Artificial Intelligence for Bias Detection in COVID CT-Scan Classifiers
Palatnik de Sousa, I., Vellasco, M. M. B. R., & Costa da Silva, E. (2021) · Sensors · doi:10.3390/s21165657 · Cited by 43
Trivedi, A., Robinson, C., Blazes, M., Ortiz, A., Desbiens, J., Gupta, S., Dodhia, R., Bhatraju, P. K., Liles, W. C., Kalpathy-Cramer, J., et al. (2022) · PLOS ONE · doi:10.1371/journal.pone.0274098 · Cited by 28
Ben Ahmed, K., Hall, L. O., Goldgof, D. B., & Fogarty, R. (2022) · IEEE Access · doi:10.1109/access.2022.3193700 · Cited by 12
Metadata
- Authors
- Yoo, S. H., Geng, H., Chiu, T. L., Yu, S. K., Cho, D. C., Heo, J., Choi, M. S., Choi, I. H., Cung Van, C., Nhung, N. V., Min, B. J., & Lee, H.
- Venue
- Frontiers in Medicine
- Publisher
- Frontiers Media SA
- Published
- 2020-07-14
- Volume
- 7
- Pages
- 427-427
- ISSN
- 2296-858X (ISSN-L)
- OpenAlex
- W3041809298
- Cited by
- 287
- References
- 5
- FWCI
- 26.33
- Type
- journal article
- Language
- en
- Open access
- gold
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- Yoo, S. H., Geng, H., Chiu, T. L., Yu, S. K., Cho, D. C., Heo, J., Choi, M. S., Choi, I. H., Cung Van, C., Nhung, N. V., Min, B. J., & Lee, H. (2020). Deep Learning-Based Decision-Tree Classifier for COVID-19 Diagnosis From Chest X-ray Imaging. Frontiers in Medicine. https://doi.org/10.3389/fmed.2020.00427
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