ARCHIVES
Research Article
Pneumonia Detection In Chest X-Rays Using Neural Networks
Dr .X.S. Asha Shiny1
B.Bhavana2
A.Jyothirmayee3
B.Sushanth4
D.Sathish5
1Associate professor, Department of Information Technology, CMR Engineering College,UGC Autonomous, Hyderabad, Telangana, India. 2,3,4,5B.Tech IV-Year , Department of Information Technology, CMR Engineering College,UGC Autonomous, Hyderabad, Telangana, India.
Published Online: January-February 2024
Pages: 30-33
Cite this article
↗ https://www.doi.org/10.59256/ijsreat.20240401006
References
1. Abbasi, A., Hoshmand-Kochi, M., Li, M. G. 7. 8., & Duong, T. Q. (n.d.). Predicting COVID-19 pneumonia severity on chest X-ray with
deep learning. Retrieved April 28, 2021, from Arxiv.org website: http://arxiv.org/abs/2005.11856v3.
2. Jain, R., Gupta, M., Tan eja, S., & Hemanth, D. J. (2021). Deep learning-based detection and analysis of COVID-19 on chest X-ray
images. Applied Intelligence, 51(3), 1690–1700.
3. N. Darapaneni et al., “Deep convolutional neural network (CNN) design for pathology detection of COVID-19 in chest X-ray images,”
in Lecture Notes in Computer Science, Cham: Springer International Publishing, 2021, pp. 211–223.
4. Wang, D., Mo, J., Zhou, G., Xu, L., & Liu, Y. (2020). An efficient mixture of deep and machine learning models for COVID-19 diagnosis
in chest X-ray images. PloS One, 15(11), e0242535.
5. Arias-Londono, J. D., Gomez-Garcia, J. A., Moro-Velazquez, L., & Godino-Llorente, J. I. (2020). Artificial intelligence applied to chest
X-ray images for the automatic detection of COVID-19. A thoughtful evaluation approach. IEEE Access: Practical Innovations, Open
Solutions, 8, 226811–226827.
6. Pan, S. Agarwal, and D. Merck, “Generalizable inter-institutional classification of abnormal chest radiographs using efficient
convolutional neural networks,” J. Digit. Imaging, vol. 32, no. 5, pp. 888–896, 2019.
7. H. H. Pham, T. T. Le, D. Q. Tran, D. T. Ngo, and H. Q. Nguyen, “Interpreting chest X-rays via CNNs that exploit hierarchical disease
dependencies and uncertainty labels,” arXiv [eess.IV], 2019.
8. Stephen, O., Sain, M., Maduh, U. J., & Jeong, D.-U. (2019). An efficient deep learning approach to pneumonia classification in
healthcare. Journal of Healthcare Engineering, 2019, 4180949.
9. T. Franquet, “Imaging of community-acquired pneumonia,” J. Thora
deep learning. Retrieved April 28, 2021, from Arxiv.org website: http://arxiv.org/abs/2005.11856v3.
2. Jain, R., Gupta, M., Tan eja, S., & Hemanth, D. J. (2021). Deep learning-based detection and analysis of COVID-19 on chest X-ray
images. Applied Intelligence, 51(3), 1690–1700.
3. N. Darapaneni et al., “Deep convolutional neural network (CNN) design for pathology detection of COVID-19 in chest X-ray images,”
in Lecture Notes in Computer Science, Cham: Springer International Publishing, 2021, pp. 211–223.
4. Wang, D., Mo, J., Zhou, G., Xu, L., & Liu, Y. (2020). An efficient mixture of deep and machine learning models for COVID-19 diagnosis
in chest X-ray images. PloS One, 15(11), e0242535.
5. Arias-Londono, J. D., Gomez-Garcia, J. A., Moro-Velazquez, L., & Godino-Llorente, J. I. (2020). Artificial intelligence applied to chest
X-ray images for the automatic detection of COVID-19. A thoughtful evaluation approach. IEEE Access: Practical Innovations, Open
Solutions, 8, 226811–226827.
6. Pan, S. Agarwal, and D. Merck, “Generalizable inter-institutional classification of abnormal chest radiographs using efficient
convolutional neural networks,” J. Digit. Imaging, vol. 32, no. 5, pp. 888–896, 2019.
7. H. H. Pham, T. T. Le, D. Q. Tran, D. T. Ngo, and H. Q. Nguyen, “Interpreting chest X-rays via CNNs that exploit hierarchical disease
dependencies and uncertainty labels,” arXiv [eess.IV], 2019.
8. Stephen, O., Sain, M., Maduh, U. J., & Jeong, D.-U. (2019). An efficient deep learning approach to pneumonia classification in
healthcare. Journal of Healthcare Engineering, 2019, 4180949.
9. T. Franquet, “Imaging of community-acquired pneumonia,” J. Thora
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