Artificial Intelligence Framework for Efficient Detection and Classification of Pneumonia Using Chest Radiography Images
Author:
Publisher
Springer Science and Business Media LLC
Subject
Biomedical Engineering,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s40846-021-00631-1.pdf
Reference28 articles.
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2. Mackenzie, G. (2016). The definition and classification of pneumonia. Pneumonia, 8(1), 14. https://doi.org/10.1186/s41479-016-0012-z
3. Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P. M., Zietz, M., Hoffman, M. M., & Xie, W. (2018). Opportunities and obstacles for deep learning in biology and medicine. J Royal Soc Inter, 15(141), 20170387. https://doi.org/10.1098/rsif.2017.0387
4. Lakhani, P., & Sundaram, B. (2017). Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology, 284(2), 574–582. https://doi.org/10.1148/radiol.2017162326
5. Maier, A., Syben, C., Lasser, T., & Riess, C. (2019). A gentle introduction to deep learning in medical image processing. Zeitschrift für Medizinische Physik., 29(2), 86–101. https://doi.org/10.1016/j.zemedi.2018.12.003
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