427
Views
1
CrossRef citations to date
0
Altmetric
Articles

Deep learning based prediction of COVID-19 virus using chest X-Ray

, , &

References

  • Roosa, K., Lee, Y., Luo, R., Kirpich, A., Rothenberg, R., Hyman, J. M., … & Chowell, G. (2020). Real-time forecasts of the COVID-19 epidemic in China from February 5 to February 24, 2020. Infectious Disease Modelling, 5, 256-263.
  • Stoecklin, S. B., Rolland, P., Silue, Y., Mailles, A., Campese, C., Simondon, A.,  …  & Yamani, E. (2020). First cases of coronavirus disease 2019 (COVID-19) in France: surveillance, investigations and control measures, January 2020. Eurosurveillance, 25(6), 2000094.
  • Bhatnagar, V., Poonia, R. C., Nagar, P., Kumar, S., Singh, V., Raja, L., & Dass, P. (2020). Descriptive analysis of COVID-19 patients in the context of India. Journal of Interdisciplinary Mathematics, 1-16.
  • Chung, M., Bernheim, A., Mei, X., Zhang, N., Huang, M., Zeng, X.,  …  & Jacobi, A. (2020). CT imaging features of 2019 novel coronavirus (2019-nCoV). Radiology, 295(1), 202-207.
  • Kanne, J. P. (2020). Chest CT Findings in 2019 Novel Coronavirus (2019-nCoV) Infections from Wuhan, China: Key Points for the Radiologist. Radiology, 295(1), 16-17.
  • Max Roser, Hannah Ritchie, Esteban Ortiz-Ospina and Joe Hasell (2020) - “Coronavirus Pandemic (COVID-19)”. Published online at OurWorldInData.org. Retrieved from: ‘https://ourworldindata.org/coronavirus’ [Online Resource].
  • Ai, T., Yang, Z., Hou, H., Zhan, C., Chen, C., Lv, W., … & Xia, L. (2020). Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology, 200642.
  • Irfan U. (2020). The math behind why we need social distancing, starting right now. Accessed at: https://www.vox.com/2020/3/15/21180342/coronavirus-COVID-19-us-social-distancing, May 15, 2020.
  • Mullis, K. B. (1990). The unusual origin of the polymerase chain reaction. Scientific American, 262(4), 56-65.
  • Tahamtan, A., & Ardebili, A. (2020). Real-time RT-PCR in COVID-19 detection: issues affecting the results. (pp. 453-454).
  • Shan, F., Gao, Y., Wang, J., Shi, W., Shi, N., Han, M., … & Shi, Y. (2020). Lung infection quantification of COVID-19 in ct images with deep learning. arXiv preprint arXiv:2003.04655.
  • Li, Y., & Xia, L. (2020). Coronavirus disease 2019 (COVID-19): role of chest CT in diagnosis and management. American Journal of Roentgenology, 1-7.
  • Pan, F., Ye, T., Sun, P., Gui, S., Liang, B., Li, L., … & Zheng, C. (2020). Time course of lung changes on chest CT during recovery from 2019 novel coronavirus (COVID-19) pneumonia. Radiology, 200370.
  • Song, Y., Zheng, S., Li, L., Zhang, X., Zhang, X., Huang, Z., … & Chong, Y. (2020). Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images. medRxiv.
  • Caruso, D., Zerunian, M., Polici, M., Pucciarelli, F., Polidori, T., Rucci, C., … & Laghi, A. (2020). Chest CT features of COVID-19 in Rome, Italy. Radiology, 201237.
  • Fontanet, A., Tondeur, L., Madec, Y., Grant, R., Besombes, C., Jolly, N., … & Temmam, S. (2020). Cluster of COVID-19 in northern France: A retrospective closed cohort study. medRxiv
  • Magal, P., & Webb, G. (2020). Predicting the number of reported and unreported cases for the COVID-19 epidemic in South Korea, Italy, France and Germany. Italy, France and Germany (March 19, 2020).
  • Reznik, A., Gritsenko, V., Konstantinov, V., Khamenka, N., & Isralowitz, R. (2020). COVID-19 fear in Eastern Europe: Validation of the Fear of COVID-19 Scale. International Journal of Mental Health and Addiction, 1-6.
  • Cohen, J. P., Morrison, P., & Dao, L. (2020). COVID-19 image data collection. arXiv preprint arXiv:2003.11597.
  • Mooney, P. (2018). Chest x-ray images (pneumonia). kaggle, Marzo.
  • Zhang, W. (1988, September). Shift-invariant pattern recognition neural network and its optical architecture. In Proceedings of annual conference of the Japan Society of Applied Physics.
  • Shridhar, A. (2017). A beginner’s guide to deep learning.
  • Krizhevsky, A., & Sutskever, I. & Hinton, G. (2012). ImageNet classification with deep convolutional neural networks. Advances in neural information processing systems (pp. 1097-1105).
  • Romanuke, V. V. (2017). Appropriate number and allocation of ReLUs in convolutional neural networks. Наукові вісті Національного технічного університету України Київський політехнічний інститут, (1), 69-78.
  • Ciresan, D. C., Meier, U., Masci, J., Gambardella, L. M., & Schmidhuber, J. (2011, June). Flexible, high performance convolutional neural networks for image classification. In Twenty-Second International Joint Conference on Artificial Intelligence.
  • Yamaguchi, K., Sakamoto, K., Akabane, T., & Fujimoto, Y. (1990). A neural network for speaker-independent isolated word recognition. In First International Conference on Spoken Language Processing.
  • Ciregan, D., Meier, U., & Schmidhuber, J. (2012, June). Multi-column deep neural networks for image classification. In 2012 IEEE conference on computer vision and pattern recognition (pp. 3642-3649). IEEE.
  • Deshpande, A. (2018). The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3). adeshpande3. github. io. Retrieved, 12-04.
  • Scherer, D., Müller, A., & Behnke, S. (2010, September). Evaluation of pooling operations in convolutional architectures for object recognition. In International conference on artificial neural networks (pp. 92-101). Springer, Berlin, Heidelberg.
  • Prabhu, R. (2018). Understanding of convolutional neural network (CNN)—deep learning.
  • Santra, A. K., & Christy, C. J. (2012). Genetic algorithm and confusion matrix for document clustering. International Journal of Computer Science Issues (IJCSI), 9(1), 322.
  • Olson, D. L., & Delen, D. (2008). Advanced data mining techniques. Springer Science & Business Media.
  • Saito, T., & Rehmsmeier, M. (2015). The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PloS one, 10(3).
  • Opitz, J., & Burst, S. (2019). Macro F1 and Macro F1. arXiv preprint arXiv:1911.03347.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.