The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches
Open accessAbstract
BACKGROUND: The novel coronavirus disease 2019 (COVID-19) constitutes a public health emergency globally. The number of infected people and deaths are proliferating every day, which is putting tremendous pressure on our social and healthcare system. Rapid detection of COVID-19 cases is a significant step to fight against this virus as well as release pressure off the healthcare system. OBJECTIVE: One of the critical factors behind the rapid spread of COVID-19 pandemic is a lengthy clinical testing time. The imaging tool, such as Chest X-ray (CXR), can speed up the identification process. Therefore, our objective is to develop an automated CAD system for the detection of COVID-19 samples from healthy and pneumonia cases using CXR images. METHODS: Due to the scarcity of the COVID-19 benchmark dataset, we have employed deep transfer learning techniques, where we examined 15 different pre-trained CNN models to find the most suitable one for this task. RESULTS: A total of 860 images (260 COVID-19 cases, 300 healthy and 300 pneumonia cases) have been employed to investigate the performance of the proposed algorithm, where 70% images of each class are accepted for training, 15% is used for validation, and rest is for testing. It is observed that the VGG19 obtains the highest classification accuracy of 89.3% with an average precision, recall, and F1 score of 0.90, 0.89, 0.90, respectively. CONCLUSION: This study demonstrates the effectiveness of deep transfer learning techniques for the identification of COVID-19 cases using CXR images.
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Cites
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Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation
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Roberts, M., Driggs, D., Thorpe, M., Gilbey, J., Yeung, M., Ursprung, S., Aviles-Rivero, A. I., Etmann, C., McCague, C., Beer, L., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00307-0 · Cited by 1,044
Self-citationTransfer learning for medical image classification: a literature review
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Self-citationDense Convolutional Network and Its Application in Medical Image Analysis
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A systematic review on AI/ML approaches against COVID-19 outbreak
Dogan, O., Tiwari, S., Jabbar, M. A., & Guggari, S. (2021) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00424-8 · Cited by 87
Metadata
- Authors
- Rahaman, M. M., Li, C., Yao, Y., Kulwa, F., Rahman, M. A., Wang, Q., Qi, S., Kong, F., Zhu, X., & Zhao, X.
- Venue
- Journal of X-Ray Science and Technology: Clinical Applications of Diagnosis and Therapeutics
- Publisher
- SAGE Publications
- Published
- 2020-07-31
- Volume
- 28
- Issue
- 5
- Pages
- 821-839
- ISSN
- 0895-3996 (ISSN-L)
- 1095-9114
- OpenAlex
- W3048886990
- Cited by
- 302
- References
- 11
- FWCI
- 24.32
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Rahaman, M. M., Li, C., Yao, Y., Kulwa, F., Rahman, M. A., Wang, Q., Qi, S., Kong, F., Zhu, X., & Zhao, X. (2020). Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches. Journal of X-Ray Science and Technology: Clinical Applications of Diagnosis and Therapeutics. https://doi.org/10.3233/xst-200715
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